A control method for output power of a hybrid energy storage system

Abstract

The application provides a control method for output power of a hybrid energy storage system, a nonlinear state space equation is established by a switching function model of a reconfigurable bidirectional Buck / Boost converter, and a system output power prediction model is derived. On this basis, a differential algebraic coupling model of output power and inductor current is constructed, and the power prediction problem is converted into an inductor current control problem. According to the operation characteristics of the hybrid energy storage system, a collaborative control architecture is designed, in which the super capacitor preferentially responds to transient power, and the battery dominates steady-state power supply. A time-varying reference power trajectory is generated by real-time monitoring of the system operation state and fusion of the target function curve. The model predictive control algorithm is introduced to realize the rapid tracking of the reference power in a rolling optimization manner, and the power distribution timing of different energy storage units is effectively coordinated. The application improves the reliability of the system while realizing the optimization of operation economy, and provides an innovative solution for the collaborative control of the energy storage system in the smart grid.

Description

Technical Field

[0001] This invention relates to a method for controlling the output power of a hybrid energy storage system, belonging to the field of energy management technology for energy storage systems. Background Technology

[0002] With rapid socio-economic development, new energy power generation based on natural resources is gradually becoming an important part of the energy system. However, new energy power generation suffers from technical bottlenecks such as the intermittency and volatility of output power. This limitation prevents it from meeting the huge market demand and seriously affects the safe and stable operation of the power distribution system. Therefore, improving the stability of new energy power generation is of great significance for technological innovation, system safety enhancement, and catalysis of industrial transformation.

[0003] Batteries, with their high energy density and large capacity, are often used as the primary power source for grid peak shaving and electric vehicles. However, their low power density and limited cycle life cause battery performance to degrade rapidly under slight power fluctuations in the grid or frequent vehicle start-stop conditions, seriously threatening system stability. In contrast, supercapacitors, based on a physical electrostatic energy storage mechanism, possess advantages in high power density and long cycle life, and can respond instantaneously to high-frequency power demands. However, their low energy density limits their ability to provide continuous power. Hybrid energy storage systems, combining batteries and supercapacitors, can theoretically compensate for the insufficient power density of batteries by using supercapacitors, while also overcoming the inherent limitation of supercapacitors' inability to provide continuous power for extended periods. However, in actual operation, hybrid energy storage systems exhibit complex characteristics of multi-timescale coupling, requiring the development of reasonable and effective energy management strategies to ensure coordinated operation between different energy storage units. Among the existing power control strategies, the control methods applicable to bidirectional Buck / Boost converters have obvious defects. Most control strategies are difficult to dynamically adjust the power output according to the actual operating state of the system. Some other strategies introduce complex intelligent algorithms to achieve real-time parameter adjustment. Although this improves the adaptability of the energy storage system to different operating conditions, it also makes the control method more cumbersome, reduces energy transmission efficiency, and results in insufficient output power stability. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for controlling the output power of a hybrid energy storage system. This method can fully leverage the advantages of each power source in the hybrid energy storage system, has a simple algorithm, and can achieve precise control of the overall output power, thereby enhancing the stability and reliability of the energy storage system.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: a method for controlling the output power of a hybrid energy storage system, comprising the following steps:

[0006] S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, a nonlinear state-space equation containing the battery pack voltage, supercapacitor terminal voltage and inductor current is established, and the prediction model is obtained by discretization using the Euler method.

[0007] S2. Based on the energy conservation relationship of the converter, a differential algebraic model of output power and inductor current is established to transform power control into inductor current tracking. Based on the model predictive control framework, a value function is constructed to realize the dynamic tracking of the energy storage system output current to the reference value.

[0008] S3. Calculate the operating power of the dynamic voltage restorer based on the instantaneous power theory, and generate reference power curves for the battery pack and supercapacitor respectively in combination with the objective function;

[0009] S4. In each control cycle, traverse the set of switch states, calculate the current value at the next moment through the prediction model, select the optimal switch vector drive converter according to the constraint optimization criterion, realize the output power tracking the reference value, and provide energy for the dynamic voltage restorer.

[0010] Furthermore, the improvement of the bidirectional Buck / Boost converter in S1 involves topology optimization. Specifically, the original output capacitor is reconnected to both ends of the battery pack / supercapacitor to form an intermediate bus support, while the output resistor is connected in parallel to the inductor side to form the output port. It includes two operating states, Kirchhoff's voltage law and current law, and the state-space equation for the dynamic relationship between the output power and the reference power is established as follows:

[0011] ;

[0012] in, This is a power reference value. For the output power of the battery pack / supercapacitor, This refers to the voltage of the battery pack / supercapacitor. For output current, This refers to the output voltage.

[0013] Simplifying the above equation, we obtain the following expression for the inductor current throughout the entire operating cycle:

[0014] ;

[0015] in, These represent two switching states of the converter: state one is 0, and state two is 1.

[0016] To reduce the complexity of power point tracking, the system output power is converted into a power distribution based on inductor current. The current model is obtained by discretizing it using the forward Euler method to obtain the current prediction model for the bidirectional Buck / Boost converter, as shown in the following expression:

[0017] ;

[0018] in, This is the predicted current value for the next moment. This is the current power reference value. Current output power Output current at the current moment. The output voltage at the current moment. The input voltage is at the current moment, and T is the unit period.

[0019] Furthermore, the specific process of S3 is as follows:

[0020] S3.1 The objective function is the sigmoid function, and its output satisfies the following formula:

[0021] ;

[0022] Where k is the time constant;

[0023] S3.2, Based on the maximum power demand on the reverse DC bus side of the dynamic voltage restorer. Combining the operating characteristics of the battery pack and the supercapacitor, a dynamic switching reference power curve for the dual energy storage units is constructed. In the combined power supply operating mode, the supercapacitor, with its rapid dynamic response capability, prioritizes the pulse power output during the startup phase of the dynamic voltage restorer. However, its capacity is limited. When the voltage compensation approaches a steady state, a power transfer strategy based on complementary attenuation functions is used to achieve smooth power switching, allowing the battery pack output power to increase from 0 to... Meanwhile, the power of the supercapacitor decays to zero in a complementary symmetrical manner, strictly maintaining the total output power of the system. ;

[0024] A reference power curve is constructed using a nonlinear trajectory generation algorithm: the battery pack power curve is described by an adjustable-gain sigmoid function to represent its asymptotic power carrying capacity, while the supercapacitor power curve is generated inversely based on symmetry. The battery pack power curve and the supercapacitor power curve form a precise complementary relationship in the time domain. Their reference power curves are shown below:

[0025] Battery pack reference power: ;

[0026] Supercapacitor reference power: .

[0027] Further, the method in S4 is as follows: The control system first samples the input voltage, output voltage, and inductor current of the energy storage system in real time. Then, in each control cycle, it traverses all the switching vectors of the converter, calculates the current value at the next moment through a predictive model, evaluates the value function value corresponding to each switching state, selects the switching state with the smallest value function value as the optimal solution, and completes the closed-loop tracking control of the reference power to provide energy for the dynamic voltage restorer. The dynamic voltage restorer includes an energy storage system, an inverter unit, and a filter circuit. The energy storage system is connected to the DC side of the inverter unit, and the AC side of the inverter unit is connected in parallel between the grid and the load through a filter inductor. The inverter unit topology includes two sets of supporting capacitors on the DC side, 12 power switching devices, 12 anti-parallel diodes, and 6 clamping diodes. The system adopts a model predictive control strategy to achieve real-time tracking of the compensation current. The predicted signal is converted into a driving pulse through a power amplifier, and finally the dynamic compensation current is output.

[0028] The specific process is as follows:

[0029] S4.1 The energy provided by the energy storage system to the dynamic voltage restorer is:

[0030] ;

[0031] in, 'This represents the total output power of the energy storage system, which is approximately equal to...' and The sum;

[0032] S4.2 After receiving sufficient power support, the inverter unit generates a synchronous compensation current based on the real-time amplitude and phase information of the grid voltage. The model achieves dynamic regulation through predictive control. The model is initialized specifically by obtaining the mathematical model of the diode-clamped three-level inverter based on Kirchhoff's voltage law.

[0033] ;

[0034] in, , , This is the three-phase grid voltage. , , For three-phase compensation current, Let be the voltage between the DC side midpoint O of the topology and the grid neutral point N. , , This is the potential difference between the output point of the three-phase inverter and the midpoint O of the DC side of the topology;

[0035] S4.3 Simplify the model by transforming it from a stationary three-phase coordinate system ABC to a two-phase coordinate system αβ:

[0036] ;

[0037] in, , ,

[0038] ;

[0039] S4.4 To effectively analyze the nonlinear characteristics of the system and enhance the robustness of the model, the Euler discretization method is used to discretize the system model:

[0040]

[0041] in, , T represents the predicted values ​​of the α-axis and β-axis at time k+1, respectively. s Represents the sampling period. , They are respectively The output voltages of the α-axis and β-axis inverters at certain times. , They are respectively The grid voltage along the α-axis and β-axis at any given time; , These are the actual compensation values ​​for the α-axis and β-axis at time k, respectively; S4.5, Establish a value function with current tracking effect as the objective:

[0042] ;

[0043] in, The reference current at time t+1 in the two-phase stationary coordinate system The predicted value, The feedback current at time t+1 in the two-phase stationary coordinate system The predicted value;

[0044] S4.6. Traverse all switch states in the topology and perform real-time evaluation and calculation of each switch state through the value function to select the optimal switch state sequence, generate PWM drive signal to compensate for voltage, and ensure rapid recovery and stable operation of the load voltage.

[0045] This invention establishes a nonlinear state-space equation encompassing battery pack voltage, supercapacitor terminal voltage, and inductor current based on the switching function model of an improved bidirectional Buck / Boost converter. A predictive model is obtained through Euler discretization. Based on the converter's energy conservation relationship, a differential algebraic model of output power and inductor current is established, transforming power control into inductor current tracking. A value function is constructed based on the model predictive control framework to achieve dynamic tracking of the energy storage system's output current to a reference value. The operating power of the dynamic voltage restorer is calculated according to instantaneous power theory, and reference power curves for the battery pack and supercapacitor are generated using the objective function. Within each control cycle, the set of switching states is traversed, and the current value for the next moment is calculated using the predictive model. Based on constrained optimization criteria, the optimal switching vector is selected to drive the converter, achieving output power tracking of the reference value and providing energy for the dynamic voltage restorer. This achieves dynamic adjustment of the power distribution ratio between the two components, enabling precise control of the system's total output power. The algorithm of this invention is simple and can achieve precise control of the total output power. It not only significantly enhances the stability and reliability of the system in the face of sudden load fluctuations, but also promotes the synergy between the battery and the supercapacitor, so that the advantages of the two can be maximized in complementary operation. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the circuit topology of the hybrid energy storage system of the present invention;

[0047] Figure 2 These are waveforms of the power output of the two branches and the total power output of the hybrid energy storage system in this invention.

[0048] Figure 3 This is a flowchart of the hybrid energy storage system of the present invention;

[0049] Figure 4 This is a schematic diagram of the overall circuit topology of the dynamic voltage restorer of the present invention;

[0050] Figure 5 This is a flowchart illustrating the overall workflow of the dynamic voltage restorer and hybrid energy storage system of this invention. Detailed Implementation

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] The present invention provides a method for controlling the output power of a hybrid energy storage system, comprising the following steps:

[0053] S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, a nonlinear state-space equation containing the battery pack voltage, supercapacitor terminal voltage and inductor current is established, and the prediction model is obtained by discretization using the Euler method.

[0054] S2. Based on the energy conservation relationship of the converter, a differential algebraic model of output power and inductor current is established to transform power control into inductor current tracking. Based on the model predictive control framework, a value function is constructed to realize the dynamic tracking of the energy storage system output current to the reference value.

[0055] S3. Calculate the operating power of the dynamic voltage restorer based on the instantaneous power theory, and generate reference power curves for the battery pack and supercapacitor respectively in combination with the objective function;

[0056] S4. In each control cycle, traverse the set of switch states, calculate the current value at the next moment through the prediction model, select the optimal switch vector drive converter according to the constraint optimization criterion, realize the output power tracking the reference value, and provide energy for the dynamic voltage restorer.

[0057] like Figure 1 As shown, the energy storage system of this invention consists of two improved bidirectional Buck / Boost converters, one supercapacitor, and one battery. Each bidirectional Buck / Boost converter comprises two switching transistors, two anti-parallel diodes, one inductor, and one capacitor. , For inductor current, , For output current, , This is the output voltage.

[0058] The output waveform of the energy storage system is as follows Figure 2 As shown, (a) is the supercapacitor output power diagram, (b) is the supercapacitor reference power diagram, (c) is the battery output power diagram, (d) is the battery reference power diagram, and (e) is the total output power of the hybrid energy storage system. To achieve coordinated operation of the two energy storage devices, the supercapacitor output power gradually decreases from its rated value to zero according to the reference curve, while the battery output power gradually increases from zero to its rated value. This control mode strictly maintains the total system output power constant at its rated value, with a continuous and smooth output waveform and excellent dynamic characteristics, effectively improving load-side voltage, frequency stability, and grid-connected power quality indicators.

[0059] like Figure 3As shown, the workflow of the energy storage system is as follows: First, a model function for an improved bidirectional Buck / Boost converter is established. To reduce the complexity of power point tracking (PPT), the PPT model is transformed into a mathematical model based on inductor current, and a prediction function is obtained using the Euler algorithm. Then, a value function is constructed with current tracking performance as the control objective. During each cycle of system operation, the control unit collects the output voltage and current of the energy storage system in real time. After inputting these values ​​into the prediction model, rolling optimization iterates through all possible switching states, calculating the predicted current value corresponding to each vector. Finally, these predicted values ​​obtained through the iterative calculations are input into the value function. Based on the reference power, the optimal vector for the current moment is selected. This optimal vector is then used as the drive signal output, achieving effective tracking of the reference power.

[0060] like Figure 4 As shown, the dynamic voltage restorer consists of an energy storage system, an inverter unit, and a filter circuit. The energy storage system is connected to the DC side of the inverter, while the AC side of the inverter is connected in parallel to the grid and the load via a filter inductor. This inverter topology includes two sets of supporting capacitors on the DC side, 12 power switching devices, 12 anti-parallel diodes, and 6 clamping diodes. The system employs a model predictive control strategy to achieve real-time tracking of the compensation current. The predicted signal is converted into drive pulses by a power amplifier, ultimately outputting the dynamic compensation current. The working principle and operation process of the dynamic voltage restorer are as follows: Figure 5 As shown, the system monitors the grid voltage in real time. When a grid fault is detected, the dynamic voltage restorer is immediately activated. During this process, the hybrid energy storage system dynamically adjusts the output power through the power converter and supplies power to the inverter unit. The inverter unit generates a synchronous compensation current based on the real-time amplitude and phase of the grid, thereby maintaining the stability of the load-side voltage.

Claims

1. A method for controlling the output power of a hybrid energy storage system, characterized in that, Includes the following steps: S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, a nonlinear state-space equation containing the battery pack voltage, supercapacitor voltage and inductor current is established, and the current prediction model is obtained by discretization using the Euler method. S2. Based on the energy conservation relationship of the converter, a differential algebraic model of output power and inductor current is established to transform power control into inductor current tracking. Based on the model predictive control framework, a value function is constructed to realize the dynamic tracking of the energy storage system output current to the reference value. S3. Calculate the operating power of the dynamic voltage restorer based on the instantaneous power theory, and generate reference power curves for the battery pack and supercapacitor respectively in combination with the objective function; S4. In each control cycle, traverse the set of switch states, calculate the current value at the next moment through the prediction model, select the optimal switch vector drive converter according to the constraint optimization criterion, realize the output power tracking of the reference value, and provide energy for the dynamic voltage restorer. The improvement of the bidirectional Buck / Boost converter in S1 involves topology optimization. Specifically, the original output capacitor is reconnected to both ends of the battery pack / supercapacitor to form an intermediate bus support, and the output resistor is connected in parallel to the inductor side to form the output port. It has two operating states. Based on Kirchhoff's voltage law and current law, the state-space equation for the dynamic relationship between the output power and the reference power is established as follows: ； in, This is a power reference value. For the output power of the battery pack / supercapacitor, This refers to the voltage of the battery pack / supercapacitor. For output current, This refers to the output voltage. Simplifying the above equation, we obtain the following expression for the state-space equation for the entire working cycle: ； in, These represent two switching states of the converter: state one is 0, and state two is 1. To reduce the complexity of power point tracking, the system output power is converted into a power distribution based on inductor current. The current model is obtained by discretizing it using the forward Euler method to obtain the current prediction model for the bidirectional Buck / Boost converter, as shown in the following expression: ； in, This is the predicted current value for the next moment. This is the current power reference value. This represents the current output power of the battery pack / supercapacitor. Output current at the current moment. The output voltage at the current moment. The voltage of the battery pack / supercapacitor is at the current moment, and T is the unit period.

2. The method for controlling the output power of a hybrid energy storage system according to claim 1, characterized in that, The specific process of S3 is as follows: S3.1 The objective function is the sigmoid function, and its output satisfies the following formula: ； Where k is the time constant; S3.

2. Constructing reference power curves using a nonlinear trajectory generation algorithm: The reference power curve of the battery pack uses an adjustable-gain sigmoid function to describe its asymptotic power carrying characteristics, while the reference power curve of the supercapacitor is generated inversely based on symmetry characteristics. The reference power curves of the battery pack and the supercapacitor form a precise complementary relationship in the time domain dimension, and their reference power curves are represented as follows: Battery pack reference power: ; Supercapacitor reference power: .

3. The method for controlling the output power of a hybrid energy storage system according to claim 2, characterized in that, The method described in S4 is as follows: The control system first samples the voltage, output voltage, and inductor current of the battery pack and supercapacitor in real time. Then, in each control cycle, it traverses all the switching vectors of the converter, calculates the current value at the next moment through the prediction model, evaluates the value function value corresponding to each switching state, selects the switching state with the smallest value function value as the optimal solution, and completes the closed-loop tracking control of the reference power to provide energy for the dynamic voltage restorer. The dynamic voltage restorer includes an energy storage system, an inverter unit, and a filter circuit. The energy storage system is connected to the DC side of the inverter unit, and the AC side of the inverter unit is connected in parallel between the grid and the load through a filter inductor. The specific process is as follows: S4.1 The energy provided by the energy storage system to the dynamic voltage restorer is: ； in, This is the total output power of the energy storage system, numerically equal to... and The sum; S4.2 After receiving sufficient power, the inverter unit generates a synchronous compensation current based on the real-time amplitude and phase information of the grid voltage. The model achieves dynamic regulation through predictive control. The model is initialized specifically by obtaining the mathematical model of the diode-clamped three-level inverter based on Kirchhoff's voltage law. ； in, , , This is the three-phase grid voltage. , , For three-phase compensation current, This is the voltage between the DC side midpoint O of the topology and the grid neutral point N; , , This is the potential difference between the output point of the three-phase inverter and the midpoint O of the DC side of the topology; S4.3 Simplify the model by transforming it from a stationary three-phase coordinate system ABC to a two-phase coordinate system αβ: ； in, , , ； S4.4 To effectively analyze the nonlinear characteristics of the system and enhance the robustness of the model, the Euler discretization method is used to discretize the system model: ； in, , T represents the predicted current values ​​along the α and β axes at time k+1, respectively. s Represents the sampling period. , They are respectively The output voltages of the α-axis and β-axis inverters at certain times. , They are respectively The grid voltage along the α-axis and β-axis at any given time; , These are the actual compensation current values ​​for the α-axis and β-axis at time k, respectively; S4.

5. Establish a value function with current tracking performance as the objective: ； in, The reference current at time t+1 in the two-phase stationary coordinate system The predicted value, The feedback current at time t+1 in the two-phase stationary coordinate system The predicted value; S4.

6. Traverse all switch states in the topology and evaluate each switch state in real time using a value function to select the optimal switch state sequence and generate a PWM drive signal to compensate for the voltage.

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