Dynamic stability coordination control method considering battery life in wind storage combined system

Abstract

The present application relates to the field of new energy power generation and intelligent energy storage control technology, especially to a dynamic stability coordination control method considering battery life in a wind storage combined system. The content includes: collecting real-time operation data of the wind storage combined system, introducing a life loss quantification mechanism based on battery health state and environmental factors dynamic weighting, calculating the battery life loss weight; based on the battery life loss weight, through a dynamic power limiting strategy, obtaining the maximum charging and discharging power of the battery and the minimum charging and discharging power of the battery; based on the battery life loss weight, combining the real-time operation data, constructing a multi-objective optimization function with switchable priority, and performing minimum solution, outputting the battery charging and discharging power instruction. The technical problems of fixed control target weight, battery life state not participating in control feedback and fixed power constraint in the traditional MPC control method are solved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and intelligent energy storage control technology, and in particular to a dynamic stability coordination control method for wind and energy storage combined systems that takes into account battery life. Background Technology

[0002] With the increasing proportion of large-scale wind power grid connection, its power fluctuations and unpredictability pose significant challenges to grid frequency stability. To improve the safety and friendliness of wind power grid connection, wind-storage integrated systems, as an important frequency regulation and power smoothing technology, have been widely researched and applied in engineering. Battery energy storage units play a buffering and supporting role in wind power regulation, but their frequent charging and discharging and variable operating conditions easily lead to rapid lifespan degradation, thus affecting the system's economy and operational safety. Currently, mainstream wind-storage coordinated control strategies are mostly based on Model Predictive Control (MPC) structures, which construct and optimize objective functions and execute power regulation to achieve frequency regulation and power smoothing objectives. However, the existing MPC methods have the following key shortcomings: (1) The control target weights are fixed and cannot be adjusted according to the operating conditions: In traditional MPC control, the weight coefficients of each objective function are mostly fixed values, and the state factors such as the degree of battery aging and the magnitude of grid frequency deviation are not considered, resulting in unreasonable response under special operating conditions such as sudden frequency fluctuations or severe battery aging, and lack of sufficient adaptability; (2) The battery life status is not involved in the control feedback: Most existing methods regard the battery as a static controllable unit, do not build a life perception mechanism, and do not consider the actual impact of the battery health status on the power execution boundary, which may lead to the issuance of high power commands when the battery is severely aged, resulting in excessive losses; (3) The power constraints are fixed and cannot be adjusted according to the battery status: Traditional constraints such as the upper and lower limits of power and the charge and discharge rate boundaries are mostly statically set, and dynamic adjustment logic related to the battery status is not introduced, making it difficult to adapt to the nonlinear decay characteristics of battery life.

[0003] Therefore, there is an urgent need to provide a dynamic, stable, and coordinated control method for wind and energy storage systems that takes into account battery life to solve the above problems. Summary of the Invention

[0004] This invention provides a dynamic stability coordination control method for wind and energy storage systems that takes into account battery life, in order to solve the technical problems of fixed control target weights, non-participation of battery life state in control feedback, and fixed power constraints in traditional MPC control methods.

[0005] The dynamic stability coordination control method for the wind-storage combined system of the present invention, which considers battery life, includes the following steps:

[0006] S1. Collect real-time operation data of the wind-storage combined system, introduce a life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors, and calculate the battery life loss weight; based on the battery life loss weight, obtain the battery's maximum charge and discharge power and minimum charge and discharge power through a dynamic power limiting strategy.

[0007] S2. Based on the battery life loss weight and combined with real-time operating data, construct a multi-objective optimization function with switchable priorities, and solve for its minimization to output battery charging and discharging power commands.

[0008] Preferably, S1 specifically includes:

[0009] In the process of implementing the life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors, a battery aging degree factor and an adaptive weight function are generated based on real-time operating data, and a life loss sensitivity adjustment factor is introduced to calculate the battery life loss weight.

[0010] Preferably, S1 specifically includes:

[0011] The adaptive weighting function is generated based on the battery state of charge and the average temperature of the battery cluster from real-time operating data.

[0012] Preferably, S1 specifically includes:

[0013] Based on the battery life loss weight, a power limiting sensitivity factor is introduced to generate a charge and discharge power limiting adjustment factor, which dynamically adjusts the boundary of the battery charge and discharge power to obtain the battery's maximum charge and discharge power and minimum charge and discharge power.

[0014] Preferably, S1 specifically includes:

[0015] The power limitation sensitivity factor is calculated by incorporating historical battery charge and discharge power, historical battery state of charge, and historical average temperature of battery clusters from historical operating data, and combining them with the least squares method.

[0016] Preferably, S2 specifically includes:

[0017] Based on real-time operating data, frequency deviation and power deviation are quantified, and a linear penalty term generated based on battery life loss weight is combined to construct a multi-objective optimization function with switchable priorities.

[0018] Preferably, S2 specifically includes:

[0019] In a multi-objective optimization function with switchable priorities, dynamic weights for different optimization objectives are introduced and dynamically adjusted based on frequency deviation.

[0020] Preferably, S2 specifically includes:

[0021] In the process of minimizing the multi-objective optimization function for switching priorities, upper and lower limits and rate of change constraints are set for the battery charging and discharging power based on the battery's maximum and minimum charging and discharging power, combined with the battery charging and discharging power in real-time operating data.

[0022] The beneficial effects of the technical solution of the present invention are:

[0023] 1. This invention proposes a life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors. By introducing battery life loss weights, the control strategy of the wind-storage combined system can be adjusted in real time according to operating conditions such as battery health status, battery state of charge and average temperature of battery clusters, thereby effectively extending battery life and improving the stability and reliability of the wind-storage combined system.

[0024] 2. This invention proposes a dynamic power limiting strategy based on battery life loss, which can adaptively compress the battery charge and discharge power boundary according to the battery life loss state, effectively suppress the charge and discharge intensity under high loss conditions, slow down the battery life decay rate, and improve the safety and economy of the coordinated control of the wind and energy storage system. It is particularly suitable for complex scenarios with drastic wind power fluctuations and frequent battery responses.

[0025] 3. This invention proposes a multi-objective optimization function with switchable priorities, which overcomes the problem that the weight coefficients of each control objective in traditional MPC are usually fixed values, resulting in unreasonable response of the wind-storage combined system under special conditions such as drastic frequency fluctuations or severe battery aging. It can automatically switch the optimization objective weights according to the actual operating conditions, so that the control strategy prioritizes frequency stability under emergency conditions, balances economy and lifespan under normal conditions, and prioritizes battery protection under stable conditions, thereby enhancing the adaptability of the wind-storage combined system control strategy and the battery life protection capability. Attached Figure Description

[0026] Figure 1 This is a flowchart of the dynamic stability coordination control method for the wind-storage combined system described in this invention, taking into account battery life. Detailed Implementation

[0027] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. 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.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] The following description, in conjunction with the accompanying drawings, details the specific scheme of the dynamic stability coordination control method for considering battery life in the wind-storage integrated system provided by this invention.

[0030] See attached document Figure 1 The diagram illustrates a flowchart of a dynamic stability coordination control method for a wind-storage integrated system considering battery life, provided by an embodiment of the present invention. The method includes the following steps:

[0031] S1. Collect real-time operation data of the wind-storage combined system, introduce a life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors, and calculate the battery life loss weight; based on the battery life loss weight, obtain the battery's maximum charge and discharge power and minimum charge and discharge power through a dynamic power limiting strategy.

[0032] Combined wind and energy storage system Real-time operational data is collected and stored in the wind power database. Specifically, the equivalent charge-discharge cycle count of the battery is collected from the energy management system. (Unit: times), dispatch given power (Unit: MW) Wind power data collected from the SCADA system (Unit: MW), power grid frequency (Unit: Hz) Battery charging and discharging power is collected from the battery management system. (Unit: MW) Battery state of charge Average temperature of battery clusters (Unit: °C)

[0033] To consider the impact of battery life on the coordinated control of the wind-storage integrated system, a life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors is proposed. This mechanism multiplies the battery aging degree factor, an adaptive weighting function, and a life loss sensitivity adjustment factor to calculate the battery life loss weight at the current moment, as shown in the following formula:

[0034] ,

[0035] in, for The weight of battery life loss at any given time; the higher the value, the higher the risk of battery life loss. express The battery aging factor at any given time can reflect the battery's health status. for The equivalent charge-discharge cycle count of the battery at any given moment. The rated number of cycles specified by the battery manufacturer is an inherent property of the battery and is obtained through a wind power database. For adaptive weighting function, it means that in The impact of battery state of charge (SOC) and average battery cluster temperature on battery lifespan is calculated. Higher values ​​indicate a greater impact of the battery's current operating state on its lifespan. The calculation method is based on battery SOC and average battery cluster temperature, using K-Means clustering to obtain representative cluster centers. Using the cluster center as the center, and employing the principle of equal spacing to set fixed-width interval boundaries, corresponding clusters are constructed. Combine intervals and calculate the mean of the historical battery aging factor under each combined interval; calculate the ratio of the mean of the historical battery aging factor under each combined interval to the minimum mean of the historical battery aging factor to obtain the adaptive weight; discretize the adaptive weight and construct a two-dimensional lookup table structure based on the battery state of charge and the average temperature of the battery cluster: when ,and ,set up ,when ,and ,set up ,when or ,and or ,set up The historical battery state of charge and the historical average temperature of the battery cluster were obtained through a wind power database. This is a life loss sensitivity adjustment factor, used to adjust the amplification effect of battery life loss weight according to different battery types, making battery life loss control more sensitive and in line with the actual battery usage conditions. It is set by professional technicians.

[0036] The above formula proposes a life loss quantification mechanism based on dynamic weighting of battery health status and environmental factors. It can calculate the battery life loss weight according to the operating conditions such as battery health status, battery state of charge and average temperature of battery cluster, and adjust the control strategy of wind and energy storage combined system in real time, thereby effectively extending battery life and improving the stability and reliability of wind and energy storage combined system.

[0037] To reduce the impact of charging and discharging processes on battery lifespan, a dynamic power limiting strategy based on battery lifespan loss is proposed, which is determined according to the weight of battery lifespan loss. Dynamically adjust battery charging and discharging power Specifically, the maximum charge / discharge power of the battery is obtained by multiplying the rated charge / discharge power by a charge / discharge power limiting adjustment factor, whereby the charge / discharge power limiting adjustment factor is based on battery life loss weighting. The specific implementation formula for the dynamic power limiting strategy based on battery life loss is as follows:

[0038] ,

[0039] Through a power limiting strategy, the minimum charge / discharge power of the battery is the negative of the maximum charge / discharge power, as shown in the following formula:

[0040] ,

[0041] in, For a moment The maximum charge and discharge power of the battery, in MW; Rated charge / discharge power of the battery, in MW, obtained from a wind power database; It is a power limiting adjustment factor for charging and discharging, which enables the battery to reach its maximum power when the battery life is low, while the power will be limited when the battery life is high, thereby avoiding overcharging and discharging that could damage the battery. The power limitation sensitivity factor is used to adjust the influence of battery life loss weight on the maximum charge and discharge power limitation of the battery. Its calculation method is as follows: Based on historical battery charge and discharge power, historical battery state of charge, and historical average temperature of battery clusters, a multiple linear regression model is constructed to calculate the theoretical value of the battery life loss weight. The error between the theoretical value and the observed value of battery life loss is fitted using the least squares method to obtain the parameters of the multiple linear regression model. The parameters corresponding to the historical battery charge and discharge power variable are used as the power limitation sensitivity factor, and their value range is mapped to [0.8, 1.5] using a linear normalization method. The observed value of the battery life loss weight is calculated using the above battery life loss weight calculation formula. The historical battery charge and discharge power is obtained from a wind power database. The multiple linear regression model is constructed based on three types of operating parameters with original adjustability and physical causality: battery charge and discharge power, battery state of charge, and average temperature of battery clusters. This avoids model failure caused by high collinearity between dependent and independent variables, and not only has engineering interpretability but also ensures the independence of parameter extraction and the effectiveness of the adjustment mechanism. for Weighting of battery life degradation at any given moment; , is the minimum charge / discharge power ratio coefficient, used to avoid the power limiting strategy outputting a negative or zero value when the battery life is greatly degraded. It is calculated by multiplying the minimum operating rate provided by the battery manufacturer by the rated capacity to obtain the minimum safe charge / discharge power of the battery, and then calculating the ratio of the minimum safe charge / discharge power of the battery to the rated charge / discharge power of the battery. The minimum operating rate and rated capacity are inherent attributes of the battery and are obtained through the wind power database. For a moment The minimum charge / discharge power of the battery, in MW;

[0042] The above formula proposes a dynamic power limiting strategy based on battery life loss, which can adaptively compress the battery charge and discharge power boundary according to the battery life loss state, effectively suppress the charge and discharge intensity under high loss conditions, slow down the battery life decay rate, and improve the safety and economy of the coordinated control of the wind and energy storage system. It is particularly suitable for complex scenarios with drastic wind power fluctuations and frequent battery responses.

[0043] S2. Based on the battery life loss weight and combined with real-time operating data, construct a multi-objective optimization function with switchable priorities, and solve for its minimization to output battery charging and discharging power commands.

[0044] Employing an MPC controller architecture, a multi-objective optimization function with switchable priorities is proposed, balancing frequency stability, power point tracking capability, and battery lifespan in the wind-storage integrated system. Weighted fusion of the squared frequency deviation, squared power deviation, and a linear penalty term based on lifespan degradation is performed. By jointly minimizing these three objectives, the MPC controller is constructed. The multi-objective optimization function at time t is given by the following formula:

[0045] ,

[0046] in, For MPC controller in The multi-objective optimization function at time step is used to measure... The degree to which the control strategy at any given time contributes to the achievement of the objectives of the wind-storage integrated system; ,express The deviation between the current grid frequency and the target grid frequency at any given time, i.e., frequency deviation, in Hz; for The power grid frequency at any given time, in Hz; The target frequency of the power grid is a fixed reference value specified by the power grid, with the unit being Hz, usually taken as 50Hz; for The square of the relative deviation of the grid frequency at any given moment reflects the grid frequency stability of the wind-storage combined system. for The square of the deviation between the combined wind and storage power at any given moment and the given dispatch power reflects the power tracking capability. express The deviation between the combined wind and storage power and the dispatched power at any given time, i.e., the power deviation, in MW; for The combined wind and energy storage power at any given time for Wind power output at any given time for Battery charging and discharging power at any given time, in MW; The power given for dispatch is in MW. for Weighting of battery life degradation at any given moment; To optimize the dynamic weights of the objective, let represent the dynamic weights of the squared frequency deviation, the squared power deviation, and the linear penalty term based on battery life loss, respectively, satisfying . and according to Frequency deviation at time The weights of each term in the objective function are dynamically adjusted to achieve priority switching of the operational objectives of the wind-storage integrated system, as follows: If This indicates that the wind-storage integrated system is in an emergency condition with severe frequency fluctuations, prioritizing frequency stability. This can be set... , , ,like This indicates that the wind-storage integrated system is in normal operating condition, employing a strategy that prioritizes both power point tracking and lifespan, and can be set... , , ,like This indicates that the wind and energy storage combined system is in a stable operating condition. At this time, switching to the battery life priority mode can be set. , , The It is the maximum frequency deviation threshold allowed by the national standard GB / T15945-2008. If it is exceeded, it is considered an abnormal situation. 0.05Hz is set by expert experience and is a commonly used small disturbance threshold in engineering.

[0047] The above formula proposes a multi-objective optimization function with switchable priorities, which overcomes the problem that the weights of each control objective in traditional MPC are usually fixed values, leading to unreasonable responses of the wind-storage combined system under special operating conditions such as drastic frequency fluctuations or severe battery aging. It can automatically switch the optimization objective weights according to the actual operating conditions, so that the control strategy prioritizes frequency stability under emergency conditions, balances economy and lifespan under normal operating conditions, and prioritizes battery protection under stable operating conditions, thereby enhancing the adaptability of the wind-storage combined system control strategy and the battery life protection capability.

[0048] To ensure that the control results are physically achievable and that the wind-storage integrated system operates safely, the MPC controller optimizes the multi-objective function. During the minimization process, upper and lower limits and a rate of change constraint are set for the battery charging and discharging power. The upper and lower limits are expressed as follows: The rate of change constraint is expressed as ;in, for Battery charging and discharging power at any given time, in MW; They are respectively Minimum and maximum charge / discharge power of the battery at any given time, in MW; This is the battery's maximum allowable power change rate, an inherent attribute of the battery, measured in MW / s, with values ​​such as 5MW / s, obtained from a wind power database.

[0049] The upper and lower limits of battery charging and discharging power constraints can ensure that the output power of the MPC controller will not exceed the safe charging and discharging power of the battery, preventing accelerated lifespan loss or hardware failure; the rate of change constraint of battery charging and discharging power can effectively suppress control command jumps and prevent inverters, battery clusters and other equipment from overload, overcurrent or abnormal response due to sudden power commands.

[0050] MPC controller for multi-objective optimization function After minimizing the solution, output the battery charging / discharging power command at the current moment. This process ensures that the wind-storage combined system can smoothly regulate power output during battery charging and discharging, thereby ensuring grid frequency stability and extending battery life.

[0051] In summary, a dynamic stability coordination control method considering battery life has been completed in the wind-storage integrated system.

[0052] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A dynamic stability coordination control method considering battery life in a wind-storage combined system, characterized in that, Includes the following steps: S1. Collect real-time operating data of the wind-storage combined system, introduce a lifespan loss quantification mechanism based on dynamic weighting of battery health status and environmental factors, generate a battery aging degree factor and adaptive weight function based on real-time operating data, and introduce a lifespan loss sensitivity adjustment factor to calculate the battery lifespan loss weight. The specific formula is as follows: ， in, for Weighting of battery life degradation at any given moment; express Battery aging factor at any given time; for The equivalent charge-discharge cycle count of the battery at any given time; The rated number of cycles specified by the battery manufacturer; For adaptive weighting function, it means that in Battery state of charge at all times and average temperature of battery clusters Impact on lifespan loss; This is a sensitivity adjustment factor for lifespan loss. Based on battery life loss weight, the maximum charge / discharge power and minimum charge / discharge power of the battery are obtained through a dynamic power limiting strategy. The dynamic power limiting strategy specifically includes: introducing a power limiting sensitivity factor, generating a charge / discharge power limiting adjustment factor, and dynamically adjusting the boundary of the battery charge / discharge power. S2. Based on real-time operating data, quantify frequency deviation and power deviation, and perform weighted fusion of the squared frequency deviation, squared power deviation, and battery life loss weight as a linear penalty term to construct a multi-objective optimization function with switchable priorities, and solve for its minimization to output battery charging and discharging power commands.

2. The dynamic stability coordination control method considering battery life in the wind-storage combined system according to claim 1, characterized in that, S1 specifically includes: The power limitation sensitivity factor is calculated by incorporating historical battery charge and discharge power, historical battery state of charge, and historical average temperature of battery clusters from historical operating data, and combining them with the least squares method.

3. The dynamic stability coordination control method considering battery life in the wind-storage combined system according to claim 1, characterized in that, S2 specifically includes: In a multi-objective optimization function with switchable priorities, dynamic weights for different optimization objectives are introduced and dynamically adjusted based on frequency deviation.

4. The dynamic stability coordination control method considering battery life in the wind-storage combined system according to claim 3, characterized in that, S2 specifically includes: In the process of minimizing the multi-objective optimization function for switching priorities, upper and lower limits and rate of change constraints are set for the battery charging and discharging power based on the battery's maximum and minimum charging and discharging power, combined with the battery charging and discharging power in real-time operating data.

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