Method for controlling output power of hybrid energy storage system

Abstract

The invention provides a method for controlling the output power of a hybrid energy storage system, and the method comprises the steps: building a nonlinear state-space equation through a switching function model of a reconstruction type bidirectional Buck / Boost converter, and deducing a prediction model of the output power of the system. On the basis, a differential algebraic coupling model of output power and inductive current is constructed, and a power prediction problem is converted into an inductive current control problem. Aiming at the operation characteristics of the hybrid energy storage system, a cooperative control architecture in which a supercapacitor preferentially responds to transient power and a storage battery dominates steady-state power supply is designed, and a time-varying reference power track is generated by monitoring the operation state of the system in real time and fusing a target function curve. A model prediction control algorithm is introduced, rapid tracking of reference power is achieved in a rolling optimization mode, and power distribution time sequences of different energy storage units are effectively coordinated. According to the method, the operation economy optimization is realized while the system reliability is improved, and an innovative solution is provided for cooperative control of the energy storage system in the intelligent power grid.

Description

Technical Field

[0001] The present invention relates to a control method for the output power of a hybrid energy storage system, belonging to the technical field of energy management of energy storage systems. Background Art

[0002] With the rapid development of social economy, the new energy power generation mode based on natural resources has gradually become an important part of the energy system. However, there are technical bottlenecks such as intermittency and volatility in the output power of new energy power generation. This limitation makes it unable to meet the huge market demand and will seriously affect the safe and stable operation of the distribution system. Therefore, improving the stability of new energy power generation is of great significance for technological benefit innovation, system safety enhancement, and industrial transformation catalysis.

[0003] The storage battery, with its high energy density and large-capacity energy storage characteristics, is often used as the main power supply in the fields of power grid peak shaving and electric vehicles. However, due to its low power density and limited cycle life, the battery performance deteriorates rapidly under the conditions of small power fluctuations in the power grid or frequent start-stop of vehicles, seriously threatening the system stability. In contrast, the supercapacitor, based on the physical electrostatic energy storage mechanism, has the advantages of high power density and long cycle life, and can instantaneously respond to high-frequency power demands. However, limited by its low energy density, it is difficult to continuously supply energy. The hybrid energy storage system formed by combining the storage battery and the supercapacitor can theoretically make up for the defect of the insufficient power density of the storage battery through the supercapacitor, and at the same time break through the inherent limitation that the supercapacitor cannot continuously supply power for a long time. However, in the actual operation process, the hybrid energy storage system presents complex characteristics of multi-time scale coupling, and a reasonable and effective energy management strategy needs to be constructed to ensure the coordinated cooperation between different energy storage units. Among the existing power control strategies, the control methods applicable to the bidirectional Buck / Boost converter have obvious defects. Most control strategies are difficult to dynamically adjust the power output according to the actual operation state of the system. Another part of the strategies introduce complex intelligent algorithms to achieve real-time parameter adjustment. Although the adaptability of the energy storage system to different working conditions is improved, the control method becomes more cumbersome, the energy transmission efficiency decreases, and the output power stability is insufficient. Summary of the Invention

[0004] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a control method for the output power of a hybrid energy storage system. This control method can give full play to the respective advantages of the power sources in the hybrid energy storage system, has a simple algorithm, and can achieve precise regulation of the total output power, enhancing the stability and reliability of the energy storage system.

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

[0006] S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, establish a non-linear state space equation including the battery pack voltage, the supercapacitor terminal voltage, and the inductor current, and obtain the prediction model through Euler method discretization;

[0007] S2. Based on the energy conservation relationship of the converter, establish a differential-algebraic model of the output power and the inductor current, transform the power control into inductor current tracking, and construct a cost function based on the model predictive control framework to achieve the dynamic tracking of the output current of the energy storage system to the reference value;

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

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

[0010] Further, the improvement of the improved bidirectional Buck / Boost converter in S1 is to perform topology optimization, specifically: connect the original output capacitor to both ends of the energy storage power supply to form an intermediate bus support, and at the same time connect the output resistor in parallel on the inductor side to form an output port. It has two working states, Kirchhoff's voltage law and current law. The state space equation establishing the dynamic relationship between the output power and the reference power is as follows:

[0011]

[0012] Among them, P ref is the power reference value, P out is the output power of the energy storage system, u in is the battery / supercapacitor voltage, i out is the output current, u out is the output voltage;

[0013] After arranging the above formula, the inductor current expression in the whole working cycle is as follows:

[0014]

[0015] Among them, S represents the two switching states of the converter, state one is 0, and state two is 1;

[0016] In order to reduce the complexity of power tracking, transform the system output power into a current model based on the inductor current i L and obtain the current prediction model of the bidirectional Buck / Boost converter through forward Euler method discretization. The expression is as follows:

[0017]

[0018] where i L (t + 1) is the predicted value of the current at the next moment, and P ref (t) is the reference power at the current moment, and P out (T) is the output power at the current moment, and i out (t) is the output current at the current moment, and u out (t) is the output voltage at the current moment, and u in (t) is the input voltage at the current moment, and T is the unit period.

[0019] Furthermore, the specific steps of S2 are as follows:

[0020] S2.1. Data sampling, result calculation, and drive output operations will cause delays, which will affect the control results. To compensate for the delay link, by adopting a two-step prediction method, historical data is added to the prediction calculation, and further prediction is made based on i L (t + 1). When the current value function increases compared to the historical value, the part that is expected to increase is added to enhance the convergence speed of the algorithm, reduce the inductor current fluctuation, and improve the system stability. Combining with the Lagrange interpolation function, the specific expression is as follows:

[0021] Z(t + 1) = 3Z(t) - 3Z(t - 1) + Z(t - 2);

[0022] where represents a function regarding the output power, voltage, and current, and Z(t - 1), Z(t - 2) represent historical data. Substituting into the current prediction model, we get:

[0023]

[0024] where i L (t + 2) is the predicted value of the inductor current at time t + 2. During the rolling optimization process, the predicted value of i L (t + 2) is used as the predicted value of the current at the next moment to participate in the calculation to achieve the best optimization effect;

[0025] S2.2. The influencing factor of the energy storage system output power control is only the current tracking effect. At the same time, to improve the accuracy of the function, an absolute error function is used to establish the value function as follows:

[0026]

[0027] where is the predicted value of the reference current at the next moment.

[0028] Further, the specific process of S3 is as follows:

[0029] S3.1. The objective function is a sigmoid function, and its output satisfies the following formula:

[0030]

[0031] where k is the time constant;

[0032] S3.2. According to the maximum power demand P max on the inverse DC bus side of the dynamic voltage restorer, combined with the working characteristics of the battery and the super capacitor, a dynamic switching reference power curve of the dual energy storage unit is constructed. In the composite power supply working mode, the super capacitor, relying on its fast dynamic response ability, preferentially undertakes the pulse power output during the starting stage of the dynamic voltage restorer. However, its capacity is limited. When the voltage compensation tends to be stable, through the power transfer strategy based on the complementary attenuation function, smooth power switching is achieved, so that the output power of the battery increases from 0 to P max , while the power of the super capacitor decays to zero in a complementary symmetric form, strictly maintaining the total system output power P total = P max ;

[0033] The reference power curve is constructed through the non - linear trajectory generation algorithm: the power curve of the battery uses a sigmoid function with adjustable gain to describe its progressive power - bearing characteristics, and the power curve of the super capacitor is generated inversely according to the symmetric characteristics. The power curve of the battery and the power curve of the super capacitor form an exact complementary relationship in the time domain dimension, and their reference power curves are respectively expressed as follows:

[0034] Battery reference power:

[0035] Super capacitor reference power:

[0036] Further, the method of 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, and then traverses all the switching vectors of the converter within each control cycle. It calculates the current value at the next moment through a prediction model, evaluates the value function values corresponding to each switching state, selects the switching state with the minimum value function value as the optimal solution, completes the closed-loop tracking control of the reference power, and provides energy for the dynamic voltage restorer. The dynamic voltage restorer includes an energy storage system, an inverter unit, and a filter circuit. Among them, 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 to the grid and the load through a filter inductor. The inverter unit topology includes 2 groups of support capacitors on the DC side, 12 power switching devices, 12 anti-parallel diodes, and 6 clamping diodes. The system uses a model predictive control strategy to achieve real-time tracking of the compensation current, converts the prediction signal into a drive pulse through a power amplifier, and finally outputs a dynamic compensation current.

[0037] The specific process is as follows:

[0038] S4.1. The energy provided by the energy storage system for the dynamic voltage restorer is:

[0039] P out =i out1 u out1 +i out2 u out2 ;

[0040] Among them, P out is the total output power of the energy storage unit, which is numerically approximately equal to the sum of P ref1 and P ref2 ;

[0041] S4.2. After receiving sufficient electrical energy support, the inverter unit generates a synchronous compensation current according to the real-time amplitude and phase information of the grid voltage. The model realizes dynamic regulation through predictive control. Initialize the model. Specifically, according to Kirchhoff's voltage law, the mathematical model of the diode-clamped three-level inverter is obtained:

[0042]

[0043] Among them, u sa , u sb , u sc are the three-phase grid voltages, i CA , i CB , i CC are the compensation currents, and u ON is the voltage between the midpoint O of the topology DC side and the neutral point N of the grid. u AO , u BO , u CO are the potential differences between the output points of the inverter and the midpoint O of the topology DC side;

[0044] S4.3. Simplify the model and transform it from the stationary three-phase coordinate system ABC to the two-phase coordinate system αβ:

[0045]

[0046] Among them,

[0047]

[0048] 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:

[0049]

[0050] Among them, i α (k), i β (k) is the actual compensation value at time k; i α (k + 1), i β (k + 1) represents the predicted value at time k + 1, T s represents the sampling period, u α , u β is the output voltage of the inverter at time k, e α , e β is the grid voltage at time k;

[0051] S4.5. Establish a value function with the current tracking effect as the goal:

[0052]

[0053] Among them, is the predicted value of the reference current in the two-phase stationary coordinate system αβ at time t + 1, i αβ (t + 1) is the predicted value of the feedback current in the two-phase stationary coordinate system αβ at time t + 1;

[0054] S4.6. Traverse all the switch states in the topological structure, and perform real-time evaluation and calculation on each switch state through the value function, screen out the optimal switch state sequence, generate a PWM drive signal to compensate the voltage, and ensure the rapid recovery and stable operation of the load terminal voltage.

[0055] Based on the switching function model of the improved bidirectional Buck / Boost converter, a nonlinear state-space equation including the battery pack voltage, the supercapacitor terminal voltage, and the inductor current is established, and a prediction model is obtained through Euler method discretization; based on the energy conservation relationship of the converter, a differential-algebraic model of the output power and the inductor current is established, the power control is transformed into inductor current tracking, and based on the model predictive control framework, a cost function is constructed to achieve the dynamic tracking of the output current of the energy storage system to the reference value; the operating power of the dynamic voltage restorer is calculated according to the instantaneous power theory, and the reference power curves of the battery and the supercapacitor are generated respectively in combination with the objective function; within each control period, the set of switching states is traversed, the current value at the next moment is calculated through the prediction model, and according to the constraint optimization criterion, the optimal switching vector is selected to drive the converter to achieve the tracking of the output power to the reference value and provide energy for the dynamic voltage restorer. The dynamic adjustment of the power distribution ratio between the two is realized, and the precise regulation of the total system output power is achieved. The algorithm of the present invention is simple and can achieve the precise regulation of the total system output power, which not only significantly enhances the stability and reliability of the system in the face of sudden load fluctuations, but also promotes the coordination between the battery and the supercapacitor, maximizing the advantages of both in complementary operation. Description of the Drawings

[0056] Figure 1 is the circuit topology schematic diagram of the hybrid energy storage system of the present invention;

[0057] Figure 2 is the waveform diagram of the power output of the two branches and the total power output of the hybrid energy storage system of the present invention;

[0058] Figure 3 is the working flow chart of the hybrid energy storage system of the present invention;

[0059] Figure 4 is the overall circuit topology schematic diagram of the dynamic voltage restorer of the present invention;

[0060] Figure 5 is the overall working flow chart of the dynamic voltage restorer and the hybrid energy storage system of the present invention. Detailed Embodiments

[0061] The present invention will be further described below with reference to the drawings.

[0062] A control method for the output power of a hybrid energy storage system provided by the present invention includes the following steps:

[0063] S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, a nonlinear state-space equation including the battery pack voltage, the supercapacitor terminal voltage, and the inductor current is established, and a prediction model is obtained through Euler method discretization;

[0064] S2. Based on the energy conservation relationship of the converter, establish a differential-algebraic model of the output power and the inductor current, transform the power control into inductor current tracking, and based on the model predictive control framework, construct a cost function to achieve the dynamic tracking of the output current of the energy storage system to the reference value;

[0065] S3. Calculate the operating power of the dynamic voltage restorer according to the instantaneous power theory, and generate the reference power curves of the battery and the supercapacitor respectively in combination with the objective function;

[0066] S4. In each control period, traverse the set of switch states, calculate the current value at the next moment through the prediction model, and select the optimal switch vector to drive the converter according to the constraint optimization criterion to achieve the tracking of the output power to the reference value and provide energy for the dynamic voltage restorer.

[0067] As Figure 1 shown, the energy storage system in the present invention consists of two improved bidirectional Buck / Boost converters, one supercapacitor and one battery. Among them, each bidirectional Buck / Boost converter is composed of 2 switching tubes, 2 anti-parallel diodes, 1 inductor and 1 capacitor. i L1 、i L2 are the inductor currents, i out1 、i out2 are the output currents, and u out1 、u out are the output voltages.

[0068] The output waveform of the energy storage system is as Figure 2 shown, where (a) is the output power diagram of the supercapacitor, (b) is the reference power diagram of the supercapacitor, (c) is the output power diagram of the battery, (d) is the reference power diagram of the battery, and (e) is the total output power of the hybrid energy storage system; to achieve the coordinated cooperation of the two energy storage devices, the output power of the supercapacitor gradually decreases from the rated value to zero according to the reference curve. At the same time, the output power of the battery gradually increases from zero to the rated value. This control mode strictly maintains the total output power of the system constant at the rated value, the output waveform is continuous and smooth, and the dynamic characteristics are excellent, effectively improving the load terminal voltage, frequency stability and grid-connected power quality indicators.

[0069] As Figure 3As shown in the figure, the working process of the energy storage system is as follows: First, a model function of the improved bidirectional Buck / Boost converter is established. To reduce the complexity of power tracking, the power tracking model is transformed into a mathematical model based on inductor current, and a prediction function is obtained through the Euler algorithm. Subsequently, a cost function is constructed with the current tracking effect as the control objective. In each cycle of the system operation, the control unit collects the output voltage and output current of the energy storage system in real time. After inputting them into the prediction model, all possible switching states are traversed through rolling optimization, and the current prediction values corresponding to each vector are calculated. Finally, these prediction values obtained through the traversal operation are input into the cost function, and the optimal vector at the current moment is selected based on the reference power. This optimal vector will be output as a driving signal to achieve effective tracking of the reference power.

[0070] As Figure 4 shown in the figure, the dynamic voltage restorer consists of an energy storage system, an inverter unit, and a filter circuit. Among them, the energy storage system is connected to the DC side of the inverter, and the AC side of the inverter is connected in parallel to the grid and the load through a filter inductor. The inverter topology includes two groups of support capacitors on the DC side, 12 power switch 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 prediction signal is converted into a driving pulse through a power amplifier, and finally, a dynamic compensation current is output. The working principle and operation process of the dynamic voltage restorer are as Figure 5 shown in the figure. The system monitors the grid voltage in real time. When a grid fault is detected, the dynamic voltage restorer is immediately started. During this process, the hybrid energy storage system dynamically adjusts the output power through a 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 terminal voltage.

Claims

1. A control method for the output power of a hybrid energy storage system, characterized in that It includes the following steps: S1. Based on the switching function model of the improved bidirectional Buck / Boost converter, establish a non-linear state space equation including the battery pack voltage, the supercapacitor terminal voltage, and the inductor current, and obtain the prediction model through Euler's method discretization; S2. Based on the energy conservation relationship of the converter, establish a differential algebraic model of the output power and the inductor current, convert the power control into inductor current tracking, and based on the model predictive control framework, construct a value function to achieve the dynamic tracking of the output current of the energy storage system to the reference value; S3. Calculate the operating power of the dynamic voltage restorer according to the instantaneous power theory, and generate the reference power curves of the battery and the supercapacitor respectively in combination with the objective function; S4. In each control period, traverse the set of switching states, calculate the current value at the next moment through the prediction model, and select the optimal switching vector to drive the converter according to the constraint optimization criterion to achieve the tracking of the output power to the reference value and provide energy for the dynamic voltage restorer.

2. The control method for the output power of the hybrid energy storage system according to claim 1, characterized in that The improvement of the improved bidirectional Buck / Boost converter in S1 is to perform topology optimization. Specifically, the original output capacitor is reconnected to both ends of the energy storage power supply to form an intermediate bus support, and at the same time, the output resistor is connected in parallel on the inductor side to form an output port. It includes two operating states, Kirchhoff's voltage law and current law. The state space equation for establishing the dynamic relationship between the output power and the reference power is as follows: Among them, P ref is the power reference value, P out is the output power of the energy storage system, u in is the battery / supercapacitor voltage, i out is the output current, u out is the output voltage; After arranging the above formula, the inductor current expression for the entire operating cycle is as follows: Among them, S represents the two switching states of the converter. State one is 0, and state two is 1; To reduce the complexity of power tracking, the system output power is converted into a current model based on the inductor current i L and discretized by the forward Euler method to obtain a current prediction model for the bidirectional Buck / Boost converter. The expression is as follows: where i L (t + 1) is the predicted current value at the next moment, P ref (t) is the reference power at the current moment, P out (t) is the output power at the current moment, i out (t) is the output current at the current moment, u out (t) is the output voltage at the current moment, u in (t) is the input voltage at the current moment, and T is the unit period.

3. The control method for the output power of the hybrid energy storage system according to claim 2, characterized in that The specific steps of S2 are as follows: S2.

1. By adopting a two-step prediction method, historical data is added to the prediction calculation, and further prediction is made on the basis of i L (t + 1). When the current value function increases compared with the historical value, the part that is expected to increase is added. Combining with the Lagrange interpolation function, the specific expression is as follows: Z(t + 1) = 3Z(t) - 3Z(t - 1) + Z(t - 2); Among them, represents a function regarding output power, voltage, and current. Z(t - 1) and Z(t - 2) represent historical data. Substituting them into the current prediction model gives: where, i L (t + 2) is the predicted value of the inductor current at the moment t + 2; during the rolling optimization process, the predicted value of i L (t + 2) is used as the predicted value of the current at the next moment to participate in the calculation to achieve the best optimization effect; S2.

2. Establish a value function using the absolute error function as follows: Among them, is the predicted value of the reference current at the next moment.

4. The control method for the output power of the hybrid energy storage system according to claim 1, characterized in that The specific process of S3 is: S3.

1. The objective function is a sigmoid function, and its output satisfies the following formula: Among them, k is the time constant; S3.

2. Construct the reference power curve through the non-linear trajectory generation algorithm: The battery power curve uses a sigmoid function with adjustable gain to describe its progressive power carrying characteristics, and the supercapacitor power curve is generated reversely according to the symmetry characteristics. The battery power curve and the supercapacitor power curve form an accurate complementary relationship in the time domain dimension, and their reference power curves are respectively expressed as follows: Reference power of the storage battery: Supercapacitor reference power:

5. The control method for the output power of the hybrid energy storage system according to claim 1, wherein The method of S4 is: The control system first samples the input voltage, output voltage, and inductor current of the energy storage system in real time, and then traverses all the switching vectors of the converter in each control period, calculates the current value at the next moment through the prediction model, evaluates the value function values corresponding to each switching state, and selects the switching state with the smallest value function value as the optimal solution to complete the closed-loop tracking control of the reference power and provide energy for the dynamic voltage restorer; The dynamic voltage restorer includes an energy storage system, an inverter unit, and a filter circuit. Among them, 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 to the grid and the load through a filter inductor; The specific process is: S4.

1. The energy provided by the energy storage system for the dynamic voltage restorer is: P out = i out1 u out1 + i out2 u out2 ; Among them, P out is the total output power of the energy storage unit, which is numerically approximately equal to the sum of P ref and P ref ; 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 realizes dynamic regulation through predictive control, and the model is initialized. Specifically, according to Kirchhoff's voltage law, the mathematical model of the diode-clamped three-level inverter is obtained: Among them, u sa 、u sb 、u sc are the three-phase grid voltages, i CA 、i CB 、i CC are the compensation currents, u ON is the voltage between the midpoint O of the topology DC side and the neutral point N of the grid; u AO 、u BO 、u CO are the potential differences between the inverter output points and the midpoint O of the topology DC side; S4.

3. Simplify the model, and transform from the stationary three-phase coordinate system ABC to the two-phase coordinate system αβ: Among them, 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: Among them, i α (k), i β (k) is the actual compensation value at time k; i α (k + 1), i β (k + 1) represents the predicted value at time k + 1, T s represents the sampling period, u α , u β is the output voltage of the inverter at time k, e α , e β is the grid voltage at time k; S4.

5. Establish a cost function with the current tracking effect as the goal: Among them, is the predicted value of the reference current in the αβ two-phase stationary coordinate system at time t+1, and i αβ (t+1) is the predicted value of the feedback current in the αβ two-phase stationary coordinate system at time t+1; S4.

6. Traverse all the switch states in the topological structure, and perform real-time evaluation and calculation on each switch state through the cost function, screen out the optimal switch state sequence, and generate a PWM drive signal to compensate the voltage.

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