A battery energy storage system capacity configuration optimization method and device based on predictive control

Abstract

The application belongs to the field of energy storage system operation and control, and specifically discloses a battery energy storage system capacity configuration optimization method and device based on predictive control. On the basis of system modeling, a rolling horizon predictive control strategy is adopted, charge and discharge control parameters are generated according to wind power prediction information and real-time operation state of the battery, and an initial capacity configuration scheme is obtained with the constraint that system joint output error meets a preset probability index. A battery life evaluation model is introduced to quantitatively represent battery life loss, and the life loss result is taken as a constraint condition. In the process of system operation, the battery health state is evaluated in real time, and the health state feedback is used for dynamically correcting the predictive control parameters and the capacity configuration constraint, forming a cooperative closed loop among the capacity configuration, operation control and battery life. While guaranteeing the grid-connected operation performance of the system, the application effectively takes into account the whole life cycle operation characteristics of the battery, and improves the rationality of the energy storage system configuration and the operation reliability.

Description

Technical Field

[0001] This invention relates to the field of energy storage system operation and control technology, specifically to a method and apparatus for optimizing the capacity configuration of a battery energy storage system based on predictive control. Background Technology

[0002] With the continuous expansion of grid-connected renewable energy sources such as wind power, their output fluctuations and uncertainties pose new challenges to the safe and stable operation of the power system. Battery energy storage systems, due to their advantages such as fast response and strong regulation capabilities, are widely used to mitigate renewable energy output fluctuations and improve the grid-connected performance of the power system. Rationally configuring the capacity of battery energy storage systems and formulating corresponding operation control strategies are among the key issues in improving the application effectiveness of energy storage systems.

[0003] In existing battery energy storage system capacity configuration methods, some schemes are based on historical data or typical operating conditions for static capacity design, which fails to fully reflect the dynamic changes in renewable energy output over time. To improve system performance, existing research has proposed introducing predictive control methods into the energy storage system's operation and scheduling process. By adjusting the charging and discharging behavior of the energy storage system through rolling time-domain optimization, the grid connection performance of the system can be improved.

[0004] On the other hand, batteries experience irreversible performance degradation during long-term charge-discharge cycles, and their lifespan characteristics significantly impact the economics and reliability of energy storage systems. To address this issue, some studies have introduced battery lifespan assessment models during capacity configuration or operational analysis phases to analyze battery lifespan degradation under different operating strategies. However, in existing methods, battery lifespan assessment is often used as a post-hoc analysis or independent evaluation tool, failing to establish effective linkage with capacity configuration and predictive control processes.

[0005] Furthermore, in actual operation, the battery health status evolves dynamically with changing operating conditions. While some existing technologies monitor or estimate battery health status, the results are mostly used for status display or operation management, and have not been fully involved in the predictive control parameter adjustment or capacity configuration decision-making process, making it difficult to achieve synergistic optimization between operation control, capacity configuration, and battery life. Therefore, how to develop a synergistic optimization method that takes into account both system operating performance and the characteristics of the battery throughout its entire life cycle still requires further research. Summary of the Invention

[0006] To address the disconnect between capacity design, operation control, and battery lifespan factors in existing battery energy storage system capacity configuration methods, this invention provides a predictive control-based method and apparatus for optimizing battery energy storage system capacity configuration. Within a predictive control framework, it comprehensively considers the characteristics of renewable energy output fluctuations, battery energy storage system operation performance, and battery lifespan and health status. By introducing lifespan assessment results and health status feedback constraints into the capacity configuration and operation control processes, it achieves synergistic optimization of capacity configuration schemes and operation control strategies. This allows the battery energy storage system to meet grid-connected operation performance requirements while also considering the battery's full life-cycle operation characteristics, thereby improving the rationality of energy storage system capacity configuration and the long-term economic efficiency and reliability of the system, thus solving the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the capacity configuration of a battery energy storage system based on predictive control, comprising the following steps: S1. System Modeling and Initialization: In the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established. The joint output error of the system is defined as the difference between the planned power and the actual output power. Initial operating parameters such as the state of charge range, charging and discharging power limit, efficiency parameters and sampling time of the battery energy storage system are set. S2, Predictive Control and Lifetime Constraint Decision Making; S3. Initial capacity configuration optimization: Under the predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage systems to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial capacity feasible solution for subsequent collaborative optimization. S4. Lifetime assessment and constraint generation; S5. Multi-objective Cooperative Constraint Optimization: Under the condition of introducing lifetime loss constraints, a multi-objective optimization model is established that includes capacity configuration cost, operating performance indicators and lifetime loss related indicators. The capacity configuration scheme and predictive control parameters are optimized in a coordinated manner, and the output is a capacity configuration scheme and corresponding control strategy that meets the system operating performance requirements and takes into account the battery lifetime constraints. S6. Real-time feedback and adaptive adjustment of battery health status: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

[0008] Preferably, the predictive control and lifetime constraint decision-making specifically includes: under the rolling time-domain predictive control framework, dynamically calculating the charging and discharging control parameters of the battery energy storage system based on wind power prediction data and the real-time operating status of the battery to generate corresponding charging and discharging power control commands; simultaneously evaluating the impact trend of the predictive control strategy on battery lifetime loss within the prediction time domain, and using the lifetime impact result as one of the constraints to limit the adjustment range of the charging and discharging control parameters; and simultaneously, the charging and discharging control parameters are adjustable parameters used to receive battery health status feedback and make dynamic corrections.

[0009] Preferably, the prediction time window of the rolling time domain is 2-3 hours, and the wind power prediction data update cycle within the prediction time window is 5-20 minutes.

[0010] Preferably, the charge / discharge control parameters are calculated from the energy deviation and battery operating state in the prediction time domain, and are used as the basic control parameters in predictive control, as follows: 1) When discharge is required and a future energy shortage is predicted to exceed the current available battery energy, the discharge coefficient is... K d The calculation formula is: ; in, SOC ( k (This refers to the current time) k The state of charge of the battery. This is the limit of the state of charge. The rated energy capacity of the battery, Discharge efficiency, T To predict the time domain, For the day-ahead dispatch power, This is the updated wind power forecast; 2) When charging is required and future energy surplus is predicted to exceed the battery's rechargeable capacity, the charging coefficient... K c The calculation formula is: ; in, This is the upper limit of the state of charge. For charging efficiency.

[0011] Preferably, in step S3, the performance index constraint is the probability of the system's combined output error remaining within a preset range for a certain period of time reaching a target threshold. Specifically, the performance index is: the probability of the system's combined output error falling within ±5% is not less than 90%.

[0012] Preferably, the lifetime assessment and constraint generation specifically includes: performing a lifetime assessment on the battery operation process under the initial capacity feasible solution and the corresponding predictive control strategy to obtain a quantitative result of battery lifetime loss. The quantitative result of lifetime loss is used to quantitatively calibrate the lifetime impact trend in the prediction time domain and serves as a constraint basis in the capacity configuration and predictive control co-optimization process.

[0013] Preferably, the step of evaluating the battery life under the feasible initial capacity solution and the corresponding predictive control strategy to obtain a quantitative result of battery life loss specifically involves: analyzing the battery state-of-charge change curve under the initial capacity configuration scheme and predictive control strategy based on the rainflow counting method, identifying battery charge-discharge cycles and calculating the corresponding depth of discharge; and converting the number of cycles at different depths of discharge into a quantitative result of life loss by querying the pre-stored battery cycle life characteristic curve.

[0014] Preferably, in step S6, the real-time assessment of battery health status specifically involves: estimating battery health status in real time based on multi-dimensional operating data of battery voltage, temperature, internal resistance, and current collected in real time using a health status assessment model.

[0015] On the other hand, to achieve the above objectives, the present invention also provides the following technical solution: a battery energy storage system capacity configuration optimization device based on predictive control, comprising the following modules: System Modeling and Initialization Module: During the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established. The joint output error of the system is defined as the difference between the planned power and the actual output power. Initial operating parameters such as the state of charge range, charging and discharging power limits, efficiency parameters, and sampling time of the battery energy storage system are set. Predictive control and lifetime-constrained decision-making module; Initial capacity configuration optimization module: Under the action of predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage system to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial capacity feasible solution for subsequent collaborative optimization; Life assessment and constraint generation module; Multi-objective collaborative constraint optimization module: Under the condition of introducing life loss constraints, a multi-objective optimization model is established, which includes capacity configuration cost, operating performance indicators and life loss related indicators. The capacity configuration scheme and predictive control parameters are collaboratively optimized, and the output capacity configuration scheme and corresponding control strategy that meet the system operating performance requirements and take into account the battery life constraints are output. Real-time battery health status feedback and adaptive adjustment module: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

[0016] The beneficial effects of this invention are: (1) This invention carries out capacity configuration and operation control of battery energy storage system under the predictive control framework. By using the rolling time domain predictive control method, it fully considers the time series characteristics of renewable energy output, improves the energy storage system's ability to adjust to output fluctuations, and is conducive to improving the grid-connected operation performance of the system.

[0017] (2) The present invention introduces the battery life assessment results as a constraint in the capacity configuration and operation control process, so that the capacity configuration scheme and control strategy are no longer based solely on the operation performance, but also take into account the battery life characteristics, thus avoiding the problem of excessive battery aging caused by short-term operation performance optimization.

[0018] (3) By considering the impact of battery life loss during the predictive control process and quantitatively constraining the life assessment results in the subsequent collaborative optimization stage, this invention achieves collaborative optimization between capacity configuration, operation control and battery life, thereby improving the operational rationality of the energy storage system at the full life cycle scale.

[0019] (4) The present invention performs real-time evaluation of battery health status during system operation and uses the health status feedback to dynamically correct predictive control parameters and capacity configuration constraints, thereby enabling the energy storage system to adaptively adjust according to changes in battery status, thus improving the reliability and stability of the system in long-term operation.

[0020] (5) The technical solution of the present invention can ensure the system's operating performance while taking into account the battery's full life cycle operating characteristics, which helps to improve the economy of battery energy storage system capacity configuration and operational reliability, and has good engineering application value. Attached Figure Description

[0021] Figure 1 A schematic diagram of the process steps for optimizing the capacity configuration of a battery energy storage system based on predictive control; Figure 2 A schematic diagram of the system structure for the combined application of wind farms and battery energy storage systems; Figure 3 A comparison graph of battery SOC and system error when using a simple controller and the predictive controller of this invention; Figure 4 A probability distribution diagram showing the system meeting performance indicators under different BESS capacity configurations; Figure 5 This is a flowchart illustrating the battery life assessment algorithm. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention provides a technical solution: a method for optimizing the capacity configuration of a battery energy storage system based on predictive control, such as... Figure 1 As shown, it includes the following steps: S1. System Modeling and Initialization: In the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established, such as... Figure 2 As shown, the combined output error of the system is defined as the difference between the planned power (day-ahead predicted power) and the actual output (actual wind power output and battery energy storage system output). The initial operating parameters of the battery energy storage system, such as the state of charge range (the upper and lower limits of the battery state of charge), charging and discharging power limits, efficiency parameters, and sampling time, are set to provide a basic model and constraints for subsequent predictive control and capacity configuration.

[0024] S2. Predictive Control and Lifetime Constraint Decision-Making: Under the rolling time-domain predictive control framework, based on wind power prediction data and the real-time operating status of the battery, the charging and discharging control parameters of the battery energy storage system are dynamically calculated to generate corresponding charging and discharging power control commands. Simultaneously, the impact trend of the predictive control strategy on battery lifetime loss is evaluated within the prediction time domain, and the lifetime impact result is used as one of the constraints to limit the adjustment range of the charging and discharging control parameters, thereby avoiding potential excessive lifetime loss due to short-term performance optimization. Furthermore, the charging and discharging control parameters are adjustable parameters, used to receive battery health status feedback during system operation and dynamically correct the predictive control strategy. Figure 3 The figure shows a comparison curve of battery SOC and system error when using a simple controller and the predictive controller of the present invention.

[0025] Furthermore, based on the rolling time-domain optimization framework, the updated wind power prediction data within the future prediction time window is periodically acquired, and the charging and discharging control parameters of the battery energy storage system are dynamically calculated by combining the real-time state of charge of the battery energy storage system and the net energy demand within the prediction time domain.

[0026] The prediction time window for the rolling time domain is 2-3 hours, and the wind power prediction data update cycle within the prediction time window is 5-20 minutes.

[0027] The charge / discharge control parameters are calculated from the energy deviation and battery operating state in the prediction time domain, and serve as the basic control parameters in predictive control, as follows: 1) When discharge is required and a future energy shortage is predicted to exceed the current available battery energy, the discharge coefficient is... K d The calculation formula is: ; in, SOC ( k (This refers to the current time) k The state of charge of the battery. This is the limit of the state of charge. The rated energy capacity of the battery, Discharge efficiency, T To predict the time domain, For the day-ahead dispatch power, This is the updated wind power forecast; 2) When charging is required and future energy surplus is predicted to exceed the battery's rechargeable capacity, the charging coefficient... K c The calculation formula is: ; in, This is the upper limit of the state of charge. For charging efficiency.

[0028] S3. Initial capacity configuration optimization: Under the predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage systems to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial feasible solution for subsequent collaborative optimization. Its capacity configuration range will be further modified after introducing lifetime loss constraints.

[0029] Furthermore, the performance indicator constraint is based on the probability of the system's combined output error remaining within a preset range reaching a target threshold. Specifically, the performance indicator is that the probability of the system's combined output error falling within ±5% is not less than 90%. Figure 4 The figure shows the probability distribution of the system meeting performance indicators under different BESS capacity configurations.

[0030] S4. Lifetime assessment and constraint generation: The battery operation process under the initial capacity feasible solution and the corresponding predictive control strategy is assessed for lifetime to obtain the quantitative results of battery lifetime loss. The quantitative results of lifetime loss are used to quantitatively calibrate the lifetime impact trend in the prediction time domain and serve as the constraint basis in the capacity configuration and predictive control co-optimization process.

[0031] Furthermore, a lifespan assessment is performed on the battery operation process under the initial capacity feasible solution and the corresponding predictive control strategy to obtain quantitative results of battery lifespan loss. The battery lifespan assessment algorithm flow is as follows: Figure 5 As shown, this specifically includes: based on the rainflow counting method, analyzing the battery state-of-charge change curves under the initial capacity configuration scheme and predictive control strategy, identifying battery charge-discharge cycles and calculating the corresponding depth of discharge; and converting the number of cycles at different depths of discharge into quantitative results of life loss by querying pre-stored battery cycle life characteristic curves.

[0032] S5. Multi-objective Cooperative Constraint Optimization: Under the condition of introducing lifespan loss constraints, a multi-objective optimization model is established, which includes capacity configuration costs (initial investment costs, operation and maintenance costs), operating performance indicators and lifespan loss-related indicators. The capacity configuration scheme and predictive control parameters are optimized in a coordinated manner, and the output is a capacity configuration scheme and corresponding control strategy that meets the system operating performance requirements and takes into account the battery lifespan constraints.

[0033] Furthermore, a combination of a multi-objective optimization algorithm based on non-dominated sorting and a reinforcement learning algorithm is used to jointly optimize the capacity allocation scheme and the corresponding predictive control parameters.

[0034] S6. Real-time feedback and adaptive adjustment of battery health status: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints. This improves the long-term operating status of the battery while meeting the system's performance requirements, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

[0035] Furthermore, the real-time assessment of battery health status specifically involves estimating the battery health status in real time using a health status assessment model based on real-time collected multi-dimensional operating data of battery voltage, temperature, internal resistance, and current.

[0036] A battery energy storage system capacity configuration optimization device based on predictive control includes the following modules: System Modeling and Initialization Module: During the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established. The joint output error of the system is defined as the difference between the planned power and the actual output power. Initial operating parameters such as the state of charge range, charge and discharge power limits, efficiency parameters, and sampling time of the battery energy storage system are set.

[0037] The predictive control and lifetime constraint decision module employs a rolling time-domain predictive control method during the predictive control phase. Based on wind power forecast data and the battery's current state of charge (SBC), it calculates the required battery charge / discharge power commands for the predicted time domain. During this process, the impact of the battery SBC change trend under the predictive control strategy on battery lifetime degradation is assessed, and the lifetime impact results are used to limit the adjustment range of charge / discharge control parameters to avoid overcharging and discharging due to short-term adjustment needs.

[0038] Initial capacity configuration optimization module: Under the action of predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage system to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial capacity feasible solution for subsequent collaborative optimization.

[0039] The lifespan assessment and constraint generation module performs lifespan assessments on the battery operation process under the initial capacity configuration scheme and corresponding predictive control strategy. Specifically, it identifies charge-discharge cycles based on the battery state-of-charge change curve and, combined with the battery cycle life characteristic curve, calculates the number of cycles at different depths of discharge to obtain a quantitative result of battery lifespan loss. This quantitative result of lifespan loss is used to quantitatively calibrate the lifespan impact trend assessment in the predictive control stage and serves as a constraint condition for subsequent collaborative optimization of capacity configuration and predictive control.

[0040] Multi-objective collaborative constraint optimization module: Under the condition of introducing life loss constraints, a multi-objective optimization model is established, which includes capacity configuration cost, operating performance indicators and life loss related indicators. The capacity configuration scheme and predictive control parameters are collaboratively optimized, and the output capacity configuration scheme and corresponding control strategy that meet the system operating performance requirements and take into account the battery life constraints are output. Real-time battery health status feedback and adaptive adjustment module: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

[0041] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0043] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0044] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0045] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the capacity configuration of a battery energy storage system based on predictive control, characterized in that, Includes the following steps: S1. System Modeling and Initialization: In the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established. The joint output error of the system is defined as the difference between the planned power and the actual output power. Initial operating parameters such as the state of charge range, charging and discharging power limit, efficiency parameters and sampling time of the battery energy storage system are set. S2, Predictive Control and Lifetime Constraint Decision Making; S3. Initial capacity configuration optimization: Under the predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage systems to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial capacity feasible solution for subsequent collaborative optimization. S4. Lifetime assessment and constraint generation; S5. Multi-objective Cooperative Constraint Optimization: Under the condition of introducing lifetime loss constraints, a multi-objective optimization model is established that includes capacity configuration cost, operating performance indicators and lifetime loss related indicators. The capacity configuration scheme and predictive control parameters are optimized in a coordinated manner, and the output is a capacity configuration scheme and corresponding control strategy that meets the system operating performance requirements and takes into account the battery lifetime constraints. S6. Real-time feedback and adaptive adjustment of battery health status: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

2. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 1, characterized in that: The predictive control and lifetime constraint decision-making specifically includes: under the rolling time-domain predictive control framework, dynamically calculating the charging and discharging control parameters of the battery energy storage system based on wind power prediction data and the real-time operating status of the battery to generate corresponding charging and discharging power control commands; simultaneously evaluating the impact trend of the predictive control strategy on battery lifetime loss within the prediction time domain, and using the lifetime impact result as one of the constraints to limit the adjustment range of the charging and discharging control parameters; at the same time, the charging and discharging control parameters are adjustable parameters used to receive battery health status feedback and make dynamic corrections.

3. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 2, characterized in that: The prediction time window of the rolling time domain is 2-3 hours, and the wind power prediction data update cycle within the prediction time window is 5-20 minutes.

4. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 2, characterized in that: The charge / discharge control parameters are calculated from the energy deviation and battery operating state in the prediction time domain, and serve as the basic control parameters in predictive control, as follows: 1) When discharge is required and a future energy shortage is predicted to exceed the current available battery energy, the discharge coefficient is... K d The calculation formula is: ； in, SOC ( k (This refers to the current time) k The state of charge of the battery. This is the limit of the state of charge. The rated energy capacity of the battery, Discharge efficiency, T To predict the time domain, For the day-ahead dispatch power, This is the updated wind power forecast; 2) When charging is required and future energy surplus is predicted to exceed the battery's rechargeable capacity, the charging coefficient... K c The calculation formula is: ； in, This is the upper limit of the state of charge. For charging efficiency.

5. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 1, characterized in that: In step S3, the performance index constraint is the probability of the system's combined output error remaining within a preset range reaching a target threshold. Specifically, the performance index is: the probability of the system's combined output error falling within ±5% is not less than 90%.

6. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 1, characterized in that: The lifetime assessment and constraint generation specifically includes: performing a lifetime assessment on the battery operation process under the initial capacity feasible solution and the corresponding predictive control strategy to obtain a quantitative result of battery lifetime loss. The quantitative result of lifetime loss is used to quantitatively calibrate the lifetime impact trend in the prediction time domain and serves as a constraint basis in the capacity configuration and predictive control co-optimization process.

7. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 6, characterized in that: The process of evaluating the battery life under the initial capacity feasible solution and the corresponding predictive control strategy to obtain the quantitative result of battery life loss is as follows: Based on the rainflow counting method, the battery state of charge change curve under the initial capacity configuration scheme and predictive control strategy is analyzed to identify the battery charge and discharge cycles and calculate the corresponding discharge depth; by querying the pre-stored battery cycle life characteristic curve, the number of cycles at different discharge depths is converted into the quantitative result of life loss.

8. The battery energy storage system capacity configuration optimization method based on predictive control according to claim 1, characterized in that: In step S6, the real-time assessment of battery health status specifically involves: estimating battery health status in real time based on multi-dimensional operating data of battery voltage, temperature, internal resistance, and current collected in real time using a health status assessment model.

9. An apparatus for optimizing the capacity configuration of a battery energy storage system based on predictive control according to any one of claims 1-8, characterized in that: Includes the following modules: System Modeling and Initialization Module: During the system initialization phase, a joint operation model of the wind farm and battery energy storage system is established. The joint output error of the system is defined as the difference between the planned power and the actual output power. Initial operating parameters such as the state of charge range, charging and discharging power limits, efficiency parameters, and sampling time of the battery energy storage system are set. Predictive control and lifetime-constrained decision-making module; Initial capacity configuration optimization module: Under the action of predictive control strategy, with the system joint output error meeting the preset probability index as the performance constraint, simulation analysis is performed on different combinations of power capacity and energy capacity of battery energy storage system to determine the minimum capacity configuration scheme that meets the performance constraint requirements, which serves as the initial capacity feasible solution for subsequent collaborative optimization; Life assessment and constraint generation module; Multi-objective collaborative constraint optimization module: Under the condition of introducing life loss constraints, a multi-objective optimization model is established, which includes capacity configuration cost, operating performance indicators and life loss related indicators. The capacity configuration scheme and predictive control parameters are collaboratively optimized, and the output capacity configuration scheme and corresponding control strategy that meet the system operating performance requirements and take into account the battery life constraints are output. Real-time battery health status feedback and adaptive adjustment module: During system operation, the battery health status is evaluated in real time, and the evaluation results are fed back to the predictive control and capacity configuration process to dynamically correct the charge and discharge control parameters and capacity configuration constraints, thereby forming a collaborative closed loop between capacity configuration, operation control and battery life, and completing configuration optimization.

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