CN116914901A - Hybrid energy storage cooperative control method and system based on model predictive control - Google Patents

Abstract

The invention provides a hybrid energy storage cooperative control method based on model predictive control, which comprises the following steps: determining a direct current micro-grid structure of the hybrid energy storage system; predicting and obtaining the predicted power required by the hybrid energy storage system at the next moment by using an outer ring model controller; dividing the predicted power by an outer loop low-pass filter to obtain power requirements of a battery and a super capacitor; according to the power demand, calculating to obtain reference values of output currents of the battery and the super capacitor, and taking the current reference values as inner ring current reference input values; and inputting the inner loop current reference input value into an inner loop controller of a battery and a super capacitor, and determining a group of switching modes meeting the outer loop power requirement and the inner loop current requirement according to the output of the inner loop controller. Therefore, the invention can be based on a double closed-loop control strategy of the power outer loop and the current inner loop, avoids complex parameter setting, has stronger stability to disturbance and shortens adjustment time.

Description

A hybrid energy storage collaborative control method and system based on model predictive control

Technical field

The present invention relates to, but is not limited to, the technical field of hybrid energy storage system control, and in particular, to a hybrid energy storage collaborative control method and system based on model predictive control.

Background technique

In recent years, with the widespread application of new energy power generation in power systems, the demand for efficient collaborative control of hybrid energy storage systems has become increasingly intense. A typical hybrid energy storage system composed of high-energy-density energy storage batteries and supercapacitors cannot simultaneously satisfy the stability and rapidity of the hybrid system under fluctuations in new energy output. Therefore, effective collaborative control methods are crucial for intermittent power stabilization of new energy sources and changes in load demand.

Traditional IP control is a double closed-loop control based on frequency division by low-pass filter power. This method has a complex control structure, weakened anti-interference, and a long parameter adjustment process. It is difficult to maintain good control characteristics after the system operating point changes. . In order to overcome the shortcomings of traditional PI control, sliding mode control (SMC) or decentralized control can be used. Sliding mode control has a variable structure and is insensitive to specific disturbances and parameter tuning. However, it has defects such as communication delay, hysteresis, and slow dynamic response, which will introduce high-frequency chattering and limit the increase in system frequency. Decentralized control is a control strategy for dynamic power sharing between hybrid energy storage. During power generation and load changes, the virtual resistance and capacitance droop coefficient act as a low-pass filter for the energy storage battery and a high-pass filter for the supercapacitor, but during When loads are frequently switched, the DC microgrid voltage based on droop control will fluctuate violently, and the voltage drop caused by line impedance will further affect the quality of the DC bus voltage.

Contents of the invention

In order to solve the above problems, the present invention provides a hybrid energy storage collaborative control method, device, terminal and storage medium based on model predictive control.

The technical solution of the present invention is implemented as follows:

A hybrid energy storage collaborative control method based on model predictive control, the method includes:

S1: Determine the DC microgrid structure of the hybrid energy storage system, which includes parallel energy storage batteries and supercapacitors;

S2: Use the outer loop model controller to predict the predicted power that the hybrid energy storage system needs to stabilize at the next moment;

S3: Divide the predicted power through the outer loop low-pass filter to obtain the power requirements of the battery and supercapacitor;

S4: According to the power requirements of the energy storage battery and supercapacitor, calculate the reference values i _Llref and i _L2ref of the battery and supercapacitor output currents, and use the current reference values i _Llref and i _L2ref as the output currents of the battery and supercapacitor. Inner loop current reference input value;

S5: Input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine a set of switching modes that meet the outer loop power demand and the inner loop current demand based on the output of the inner loop controller, Thereby controlling the hybrid energy storage system.

Further, the method also includes:

S21: According to the constant-speed approach method of bus voltage recovery of the system supercapacitor, calculate the output current of the hybrid system at K+1 moment, expressed as:

Among them, k is the sampling time, i _C (k+1) is the DC bus capacitance current at k + 1 time, i _load (k) is the load current at time k, i _PV (k) is the power supply output current at time k, and C is DC bus capacitance, T _s is the sampling cycle time of the predictive controller, N is the number of bus voltage approach steps, V _dcref is the DC bus target voltage, V _dc (k) is the DC bus voltage at time k, i _ref ( k+1) is the output current of the hybrid energy storage system at time k+1;

S22: According to the output current i _ref (k+1) of the hybrid energy storage system, obtain the predicted power of the hybrid energy storage system at time k+1, which is expressed as:

p _ref (k+1)＝V _dcref ·i _ref (k+1)

The predicted power is the power required to be stabilized by the hybrid energy storage system.

Further, the method also includes:

The predicted power p _ref (k+1) is used as the input of the outer loop power low-pass filter, and the output p _bat (k+1) of the outer loop power low-pass filter is the power demand of the energy storage battery; p _ref (k+ 1) Subtract the low-pass filter output to get p _sc (k+1) which is the power requirement of the supercapacitor.

Further, the method also includes:

The reference values i _Llref and i _L2ref of the energy storage battery and supercapacitor output current are calculated as:

Among them, V _bat is the terminal voltage of the energy storage battery, and V _sc is the terminal voltage of the supercapacitor.

Further, the method also includes:

S51: Determine the current-to-duty cycle transfer function G _{id_bat} of the hybrid storage system under the small-signal model, which is expressed as:

Among them, D _bat is the duty cycle, i _bat is the actual output current of the battery, C is the supercapacitor size, R is the load size, L ₁ is the first inductance size of the battery, V _dc is the DC bus voltage, s and d are both given Value;

S52: Adjust the parameters of the PI controller transfer function for inner loop current tracking. The PI controller transfer function G _pi is expressed as:

Among them, K _P is the proportional coefficient, K _i is the integral coefficient, and K _P and K _i are parameters to be tuned;

S53: Use the inner loop current reference input value as the transfer function G _{id_bat} and the inner loop controller composed of the transfer function G _pi of the PI controller to obtain the actual output current i _bat ;

S54: According to the output of the inner loop controller, determine a set of switching modes that meet the outer loop power demand and the inner loop current demand.

Further, the method also includes:

First, calculate the output power in all switching modes in the next sampling period, then calculate the output current of the energy storage battery and supercapacitor in the next sampling period based on the obtained output power, and finally select a set of switching mode outputs that are closest to the power demand and current demand. to the switching tube of the main circuit of the energy storage battery or the main circuit of the supercapacitor to realize MPC-PI control of each group of hybrid energy storage systems.

Embodiments of the present invention also provide a hybrid energy storage collaborative control system based on model predictive control, including:

An initial determination module, used to determine the DC microgrid structure of a hybrid energy storage system, which includes parallel energy storage batteries and supercapacitors;

The power prediction module is used to predict the predicted power required to be stabilized by the hybrid energy storage system at the next moment by using the outer loop model controller;

The power prediction module is also used to divide the predicted power through an outer loop low-pass filter to obtain the power requirements of the energy storage battery and supercapacitor;

A current prediction module, configured to calculate the reference values i _L1ref and i _L2ref of the battery and supercapacitor output currents according to the power requirements of the energy storage battery and the supercapacitor, and use the current reference values i _L1ref and i _L2ref as the battery and the inner loop current reference input value of the supercapacitor;

A control module configured to input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine a set of switches that meet the outer loop power demand and the inner loop current demand based on the output of the inner loop controller. mode to control the hybrid energy storage system.

Embodiments of the present invention provide a hybrid energy storage collaborative control method based on model predictive control to determine the DC microgrid structure of a hybrid energy storage system that includes parallel energy storage batteries and supercapacitors; applying an outer loop model The controller predicts and obtains the predicted power that the hybrid energy storage system needs to stabilize at the next moment; divides the predicted power through the outer loop low-pass filter to obtain the power requirements of the battery and supercapacitor; according to the energy storage battery and the power demand of the supercapacitor, calculate the reference value of the output current of the battery and supercapacitor, and use the current reference value as the inner loop current reference input value of the system energy storage battery and supercapacitor; input the inner loop current reference The value is used as input to the inner loop controller; a set of switching modes that meet the outer loop power demand and the inner loop current demand are determined according to the output of the inner loop controller, thereby controlling the hybrid energy storage system. In this way, the present invention can be based on the double closed-loop control strategy of the power outer loop and the current inner loop, avoid complex parameter settings, have stronger stability against disturbances, and shorten the adjustment time.

Description of the drawings

The present invention will be further described below in conjunction with the accompanying drawings and examples. In the accompanying drawings:

Figure 1 is a schematic flow chart of a hybrid energy storage collaborative control method based on model predictive control in an embodiment of the present invention;

Figure 2 is a DC microgrid topology diagram of a hybrid energy storage system in an embodiment of the present invention;

Figure 3 is a schematic diagram of bus voltage recovery steps in an embodiment of the present invention;

Figure 4 is a control schematic diagram of an outer loop low-pass filter in an embodiment of the present invention;

Figure 5 is an energy storage battery current inner loop control block diagram in an embodiment of the present invention;

Figure 6 is a schematic structural diagram of a hybrid energy storage collaborative control system based on model predictive control in an embodiment of the present invention;

Figure 7 is a schematic diagram of the simulation results of the new energy photovoltaic power generation output power of a hybrid energy storage collaborative control method based on model predictive control in an embodiment of the present invention;

Figure 8 is a schematic diagram of the simulation results of load power fluctuations of a hybrid energy storage collaborative control method based on model predictive control in an embodiment of the present invention;

Figure 9 is a schematic diagram of the simulation results of hybrid energy storage power output prediction based on a model predictive control-based hybrid energy storage collaborative control method in an embodiment of the present invention;

Figure 10 is a schematic diagram of the simulation results of high and low frequency power distribution of hybrid energy storage based on a model predictive control-based hybrid energy storage collaborative control method in an embodiment of the present invention;

Figure 11 is a schematic diagram of the traditional PI double-loop control effect added as a comparative experiment in the embodiment of the present invention;

Figure 12 is a schematic diagram of the MPC-PI control effect of the present invention added as a comparative experiment in the embodiment of the present invention.

Detailed ways

In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

In the following description, suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention and have no specific meaning in themselves. Therefore, "module", "component" or "unit" may be used interchangeably.

Please refer to Figure 1. An embodiment of the present invention provides a hybrid energy storage collaborative control method based on model predictive control, which specifically includes the following steps:

S1: Determine the DC microgrid structure of the hybrid energy storage system, which includes parallel energy storage batteries and supercapacitors;

S2: Use the outer loop model controller to predict the predicted power that the hybrid energy storage system needs to stabilize at the next moment;

S3: Divide the predicted power through the outer loop low-pass filter to obtain the power requirements of the energy storage battery and supercapacitor;

S4: According to the power requirements of the energy storage battery and supercapacitor, calculate the reference values i _L1ref and i _L2ref of the battery and supercapacitor output currents, and use the current reference values i _L1ref and i _L2ref as the output currents of the battery and supercapacitor. Inner loop current reference input value;

S5: Input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine a set of switching modes that meet the outer loop power demand and the inner loop current demand based on the output of the inner loop controller, Thereby controlling the hybrid energy storage system.

The method described in the embodiment of the present invention is executed by the terminal. The terminal may be various types of terminals; for example, the terminal may be, but is not limited to, at least one of the following: a server, a computer, a tablet, or other electronic equipment.

Further, the step S1 includes:

S11: Determine the DC microgrid structure, including a hybrid energy storage system in which an energy storage battery and a supercapacitor are connected in parallel. The energy storage battery includes a first inductor and a second inductor;

S12: Establish a mathematical model of the hybrid energy storage system according to the circuit mode corresponding to the switch in the DC microgrid structure, as follows:

Among them, L ₁ is the size of the first inductor, L ₂ is the size of the second inductor, i _L1 is the current flowing through the first inductor, i _L2 is the current flowing through the second inductor, V _BAT is the energy storage battery voltage, and V _sC is the super Capacitor voltage, V _dc is the system output voltage, SW ₁ and SW ₃ are both in switching mode.

For example, the DC microgrid topology diagram of a hybrid energy storage system shown in Figure 2 includes a hybrid energy storage system in which energy storage batteries and supercapacitors are connected in parallel. L ₁ is the size of the first inductor, L ₂ is the size of the second inductor, i _L1 is the current flowing through the first inductor, i _L2 is the current flowing through the second inductor, V _BAT is the energy storage battery voltage, V _SC is the supercapacitor voltage , V _dc is the system output voltage, SW ₁ is the first switching mode, SW ₂ is the second switching mode, SW ₃ is the third switching mode, SW ₄ is the fourth switching mode, and the switching mode takes the value 0 Or 1, SW ₁ and SW ₂ have opposite values, SW ₃ and SW ₄ have opposite values.

Further, the step S2 includes:

S21: According to the constant-speed approach method of bus voltage recovery of the system supercapacitor, calculate the output current of the hybrid system at K+1 moment, expressed as:

Among them, k is the sampling time, i _C (k+1) is the DC bus capacitance current at k + 1 time, i _load (k) is the load current at time k, i _PV (k) is the power supply output current at time k, and C is DC bus capacitance, T _s is the sampling cycle time of the predictive controller, N is the number of bus voltage approach steps, V _dcref is the DC bus target voltage, V _dc (k) is the DC bus voltage at time k, i _ref ( k+1) is the output current of the hybrid energy storage system at time k+1;

S22: According to the output current i _ref (k+1) of the hybrid energy storage system, obtain the predicted power of the hybrid energy storage system at time k+1, which is expressed as:

p _ref (k+1)＝V _dcref ·i _ref (k+1) (1.13)

Among them, p _ref (k+1) is the predicted power of the system at time k+1, V _dcref is the target DC bus voltage, and i _ref (k+1) is the output current of the system at time K+1; the predicted power is The power required for stabilization by the hybrid energy storage system.

Further, the step S21 also includes:

S21a: According to the constant-speed approach method of bus voltage recovery of the system supercapacitor, the voltage prediction value of the supercapacitor at the K+1 moment of the hybrid energy storage system is obtained, which is expressed as:

Among them, N is the number of approaching steps.

Specifically, the value of the number of approaching steps N can be determined by setting the maximum steady-state tracking error:

For example, as shown in the schematic diagram of the bus voltage recovery steps in Figure 3, V _dc (k) is the voltage value of the supercapacitor terminal at sampling time k, and V _dc (k+1) is the voltage value at the supercapacitor terminal at time k + 1. The _{voltage value} of The bus voltage recovery step is designed with a constant speed approach method for voltage recovery.

S21b: The current i _C flowing through the bus capacitor can be expressed as:

Among them, C is the size of the supercapacitor, and u is the terminal voltage of the supercapacitor.

S21c: According to the change of bus voltage, the change of capacitance current can be deduced. By discretizing equation (0.4), we can get:

Among them, i _C (k+1) is the current flowing through the supercapacitor at system k+1, T _s is the sampling cycle time of the predictive controller, and V _dc (k+1) is the supercapacitor at system k+1. The terminal voltage of , V _dc (k) is the terminal voltage of the supercapacitor at time k of the system.

S21d: According to equations (0.6) and (0.7), calculate the DC bus capacitance current of the hybrid system at K+1, expressed as:

Among them, k is the sampling time, i _C (k+1) is the DC bus capacitance current at k + 1 time, i _load (k) is the load current at time k, i _PV (k) is the power supply output current at time k, and C is DC bus capacitance, T _s is the sampling cycle time of the predictive controller, N is the number of bus voltage approach steps, V _dcref is the DC bus target voltage, V _dc (k) is the DC bus voltage at time k;

S21e: New energy photovoltaic power generation uses maximum power point tracking power, and the battery output current k at the sampling time is:

Among them, V _{PV_MPPT} is the voltage corresponding to the maximum power point of photovoltaic power generation, and P _PV (k) is the maximum power point tracking power of the battery;

S21f: According to Kirchhoff’s current law, the output current of the hybrid energy storage system can be derived:

i _ref ＝i _c +i _load -i _PV (0.9)

Among them, i _ref is the output current of the hybrid energy storage system, i _load is the load current at the sampling time, and i _PV is the battery output current at the sampling time;

It should be noted that Kirchhoff's current law is that the sum of currents flowing into any node in the circuit is equal to the sum of currents flowing out of the node.

S21g: Combining equations (0.10), (0.11) and (1.11), the output current i _ref (k+1) of the hybrid energy storage system at the next moment can be obtained, which is expressed as:

Among them, i _ref (k+1) is the output current of the hybrid energy storage system at time k + 1, i _load (k) is the load current at sampling time k, and i _PV (k) is the battery output current at sampling time k;

S21i: According to the hybrid energy storage system output current i _ref (k+1), obtain the predicted power p _ref (k+1) of the hybrid energy storage system at time k+1.

In this way, embodiments of the present invention can design a prediction model based on the topology of the hybrid energy storage system based on the bus voltage dynamic recovery capability, perform predictive control on the power output of the hybrid energy storage system, and ensure that when the new energy output fluctuates and the load fluctuates, The bus voltage is stable, thereby improving the stability of the entire system operation. Moreover, predictive control of the power output of the hybrid energy storage system under the limit of the bus voltage reference can ensure that the bus overshoot is minimized, photovoltaic new energy is rationally utilized, and resources are saved.

Further, the step S3 includes:

S31: Use the predicted power p _ref (k+1) as the input of the outer loop power low-pass filter, and the output p _bat (k+1) of the outer loop power low-pass filter is the power demand of the energy storage battery;

Here, the transfer function of the outer loop power low-pass filter can be expressed as:

Among them, ω _c is the cut-off frequency; the predicted power is distributed to the high-frequency part and the low-frequency part, which can be expressed as:

Among them, P _ref is the hybrid energy storage reference power, P _bat and P _sc respectively, the energy storage battery output power, and the supercapacitor output power.

S32: p _ref (k+1) minus the low-pass filter output gives p _sc (k+1), which is the power requirement of the supercapacitor.

For example, as shown in the outer loop low-pass filter control schematic diagram in Figure 4, the power p _ref is frequency-divided and decoupled through the outer loop low-pass filter to obtain high-frequency power P _sc and low-frequency power P _bat .

Further, the step S3 also includes: according to equations (1.3) and (1.13), obtain the power requirements of the energy storage battery and supercapacitor at the next moment, which is expressed as:

P _sc (k+1)＝p _ref (k+1)-P _bat (k+1) (0.16)

Among them, p _bat (k+1) is the power demand of the energy storage battery at the next moment of the system, and p _sc (k+1) is the power demand of the supercapacitor at the next moment of the system.

It is understandable that the filter has the characteristics of simple design and fast response speed. In order to ensure that the energy storage battery can withstand the equivalent power after low-pass filtering, the selection of the cut-off frequency ω _c should be greater than or equal to the response time of the energy storage battery. At the same time, it needs Ensure that high-frequency power fluctuations can be effectively separated as much as possible. In this way, embodiments of the present invention can decouple high- and low-frequency power through low-pass filters. High-frequency power fluctuations are absorbed or provided by supercapacitors, and low-frequency power fluctuations are absorbed or provided by energy storage batteries, achieving dynamic power sharing with hybrid energy storage.

Further, in step S4, the reference values i _Llref and i _L2ref of the energy storage battery and supercapacitor output currents are calculated as:

Among them, V _bat is the terminal voltage of the energy storage battery, and V _sc is the terminal voltage of the supercapacitor.

Further, the step S5 includes:

S51: Determine the current-to-duty cycle transfer function G _{id_bat} of the hybrid storage system under the small-signal model, which is expressed as:

Among them, D _bat is the duty cycle, i _bat is the actual output current of the battery, C is the supercapacitor size, R is the load size, L ₁ is the first inductance size of the battery, V _dc is the DC bus voltage, s and d are both given Value;

S52: Adjust the parameters of the PI controller transfer function for inner loop current tracking. The PI controller transfer function G _pi is expressed as:

For example, as shown in the energy storage battery current inner loop control block diagram in Figure 5, the inner loop current tracking adopts proportional integral control. The transfer function of the inner loop current low-pass filter includes the transfer function G of current to duty cycle. _{id_bat} and PI controller G _pi , the PI controller G _pi can be expressed as:

Among them, KP is the proportional coefficient, Ki is the integral coefficient, and both KP and Ki are parameters to be tuned;

S53: Use the inner loop current reference input value as the transfer function G _{id_bat} and the inner loop controller composed of the transfer function G _pi of the PI controller to obtain the actual output current i _bat ;

S54: According to the output of the inner loop controller and the mathematical model of the hybrid energy storage system, determine a set of switching modes that meet the outer loop power demand and the inner loop current demand.

Further, the step S51 also includes:

S51a: Determine the system state equation of the energy storage battery bidirectional DC-DC circuit model, which is expressed as:

Among them, i _O is the actual DC-DC output current, and D _bat is the duty cycle;

S51b: Based on the system state equation, establish a small signal model of the energy storage battery bidirectional DC-DC circuit model, and obtain the transfer function G _{id_bat} of the system's current to duty cycle under the small signal model.

Further, the step S52 also includes: adjusting the parameters of the PI controller transfer function of the inner loop current tracking through the small signal model of the energy storage battery bidirectional DC-DC circuit model.

It can be understood that the current inner loop control block diagram and transfer function of the supercapacitor can be obtained similarly.

Further, the step S53 also includes: using the deviation between the inner loop current reference input value and the actual output current as the transfer function G _{id_ba1} and the inner loop controller composed of the transfer function G _pi of the PI controller to obtain the actual Output current i _bat .

Further, the step S54 also includes:

S54a: Based on the mathematical model of the hybrid energy storage system, that is, equation (0.21), obtain the inner loop current output model of the hybrid energy storage system, which is expressed as:

S54b: Substitute the inner loop predicted current i _Llref (k+1) and i _L2ref (k+1) into the inner loop current output model of the hybrid energy storage system to determine a set of switches that meet the outer loop power demand and the inner loop current demand. modal.

It can be understood that the model predictive control method provided by the present invention is specifically: first, calculate the output power in all switching modes in the next sampling period, and then calculate the output current of the energy storage battery and supercapacitor in the next sampling period based on the obtained output power. , and finally select a group of switching modes closest to the power demand and current demand to output to the switching tubes of the energy storage battery main circuit or supercapacitor main circuit to achieve MPC-PI control of each group of hybrid energy storage systems.

It should be noted that the embodiment of the present invention considers the impact of new energy output and load changes on DC bus voltage changes during the entire control process, and designs a double closed-loop control strategy of power outer loop and current inner loop. In this way, the embodiment of the present invention can improve the speed of control based on the double closed-loop control strategy of the power outer loop and the current inner loop. Compared with the traditional PI double closed loop, it avoids complex parameter setting and has stronger stability against disturbances. , and shorten the adjustment time.

Referring to Figure 6, an embodiment of the present invention also provides a hybrid energy storage collaborative control system based on model predictive control. The system includes: an initial determination module 201, a power prediction module 202, a current prediction module 203, and a control module 204; in,

The initial determination module 201 is used to determine the DC microgrid structure of a hybrid energy storage system, which includes parallel energy storage batteries and supercapacitors;

The power prediction module 202 is used to use an outer loop model controller to predict the predicted power that the hybrid energy storage system needs to stabilize at the next moment;

The power prediction module 202 is also used to divide the predicted power through an outer loop low-pass filter to obtain the power requirements of the energy storage battery and supercapacitor;

The current prediction module 203 is used to calculate the reference values i _Llref and i _L2ref of the battery and supercapacitor output currents according to the power requirements of the energy storage battery and the supercapacitor, and calculate the current reference values i _L1ref and i _L2ref serves as the inner loop current reference input value of the battery and supercapacitor;

The control module 204 is used to input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine the output of the inner loop controller to meet the outer loop power demand and the inner loop current demand. A set of switching modes to control the hybrid energy storage system.

In some embodiments, the method further includes:

The power prediction module is used to calculate the output current of the hybrid system at K+1 time according to the bus voltage recovery method of the system supercapacitor at a constant speed, which is expressed as:

Among them, k is the sampling time, i _C (k+1) is the DC bus capacitance current at k + 1 time, i _load (k) is the load current at k time, i _pV (k) is the power supply output current at k time, and C is DC bus capacitance, T _s is the sampling cycle time of the predictive controller, N is the number of bus voltage approach steps, V _dcref is the DC bus target voltage, V _dc (k) is the DC bus voltage at time k, i _ref ( k+1) is the output current of the hybrid energy storage system at time k+1;

The power prediction module is used to obtain the predicted power of the hybrid energy storage system at time k+1 based on the output current i _ref (k+1) of the hybrid energy storage system, which is expressed as:

p _ref (k+1)＝V _dcref ·i _hess (k+1)

The predicted power is the power required to be stabilized by the hybrid energy storage system.

In some embodiments, the method further includes:

The power prediction module is used to input the predicted power p _ref (k+1) to the outer loop power low-pass filter, and the output p _bat (k+1) of the outer loop power low-pass filter is the energy storage battery's Power requirement; p _ref (k+1) minus the low-pass filter output gives p _sc (k+1) which is the power requirement of the supercapacitor.

In some embodiments, the method further includes:

The power prediction module is used to calculate the reference values iL1ref and iL2ref of the energy storage battery and supercapacitor output current as:

Among them, V _bat is the terminal voltage of the energy storage battery, and V _sc is the terminal voltage of the supercapacitor.

In some embodiments, the method further includes:

The current prediction module is used to determine the current-to-duty cycle transfer function G _{id_bat} of the hybrid storage system under the small signal model, which is expressed as:

Among them, D _bat is the duty cycle, i _bat is the actual output current of the battery, C is the supercapacitor size, R is the load size, L ₁ is the first inductance size of the battery, V _dc is the DC bus voltage, s and d are both given Value;

The current prediction module is used to adjust the parameters of the PI controller transfer function for inner loop current tracking. The PI controller transfer function G _pi is expressed as.

Among them, K _P is the proportional coefficient, K _i is the integral coefficient, and K _P and K _i are parameters to be tuned;

The current prediction module is used to use the inner loop current reference input value as the transfer function G _{id_bat} and the inner loop controller composed of the transfer function G _pi of the PI controller to obtain the actual output current i _bat ;

The current prediction module is used to determine a set of switching modes that meet the outer loop power demand and the inner loop current demand according to the output of the inner loop controller.

As an example, the present invention compares the proposed method with other methods. specific:

Using photovoltaics as the new energy power input and using a constant power load, the effectiveness of the control strategy of the hybrid energy storage system control method (MPC-PI control method) based on model predictive control provided by the present invention is verified. The experimental results are shown in Figures 6, 7, and 8, which verify the reliability of the MPC-PI control method.

Specifically, the photovoltaic control method uses maximum power point tracking, the light intensity will simulate the actual change, and the load power will be switched from the initial 50W to 250W at 0.5s. The circuit topology parameters are set as follows: V _bat =24 (V), rated capacity 50 (Ah), response time 0.2s, V _Sc =15 (V), inductors L ₁ and L ₂ are both 0.2mH, and the bus capacitance is 3mF. Consider the response speed of the energy storage battery and its own capacity low-pass filter cutoff frequency ω _c =2 (Hz). By adjusting the controller parameters, the parameters of the energy storage battery current inner loop PI controller are finally determined to be K _p =15, K _i =50, and the supercapacitor current inner loop PI controller parameters are K _p =19.25, K _i =100.

The simulation results of new energy photovoltaic power generation output power are shown in Figure 7. From top to bottom in the figure are photovoltaic output power, input light intensity, output voltage, and output current. It can be seen from the figure that illumination changes are added to the simulation to simulate fluctuations in photovoltaic power output due to weather and other reasons. At the same time, the simulation results of load power fluctuations are shown in Figure 8, which simulates the uncertainty of load power changes. The simulation results of hybrid energy storage power output prediction obtained through model predictive control are shown in Figure 9.

The reference power of the energy storage battery and supercapacitor is obtained by decoupling the filter. The simulation results of high and low frequency power distribution of hybrid energy storage are shown in Figure 10. It can be seen from the figure that the energy storage responds due to its own characteristics when the system starts. At slower speeds, power output changes more slowly. The supercapacitor responds quickly and can promptly absorb or support transient power during each power fluctuation, smoothing the power demand of the energy storage battery and further reducing bus voltage fluctuations.

The simulation results of bus voltage stability based on PI double-loop control and traditional PI-PI control are shown in Figure 11. In the figure, the solid line with a circle represents the tracking signal of the PI-PI control system, and the solid line without a circle represents the PI- Reference signal of PI control system. The simulation results based on the DC bus voltage stability under MPC-PI control are shown in Figure 12. In the figure, the solid line with a circle represents the tracking signal of the MPC-PI control system, and the solid line without a circle represents the reference of the MPC-PI control system. Signal. Comparing Figure 11 and Figure 12, it can be seen that based on model predictive control, that is, the system under MPC-PI control has a short rise time and small overshoot. The bus voltage can still be stable at the reference voltage in the case of photovoltaic power generation and load fluctuations. . The simulation example verified that the hybrid energy storage system ensures dynamic power sharing under the MPC-PI control strategy, while quickly responding to load changes and other situations. The bus voltage fluctuation is very small, and the stability of the system is significantly improved.

It should be noted that the technical solutions recorded in the embodiments of the present invention can be combined arbitrarily as long as there is no conflict.

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (7)

1. A hybrid energy storage collaborative control method based on model predictive control, characterized in that the method includes the following steps: S1: Determine the DC microgrid structure of the hybrid energy storage system, which includes parallel energy storage batteries and supercapacitors; S2: Use the outer loop model controller to predict the predicted power that the hybrid energy storage system needs to stabilize at the next moment; S3: Divide the predicted power through the outer loop low-pass filter to obtain the power requirements of the battery and supercapacitor; S4: According to the power requirements of the energy storage battery and supercapacitor, calculate the reference values i _L1ref and i _L2ref of the battery and supercapacitor output currents, and use the current reference values i _L1ref and i _L2ref as the output currents of the battery and supercapacitor. Inner loop current reference input value; S5: Input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine a set of switching modes that meet the outer loop power demand and the inner loop current demand based on the output of the inner loop controller, Thereby controlling the hybrid energy storage system. 2. A hybrid energy storage collaborative control method based on model predictive control according to claim 1, characterized in that step S2 includes: S21: According to the constant-speed approach method of bus voltage recovery of the system supercapacitor, calculate the output current of the hybrid system at K+1 moment, expressed as: Among them, k is the sampling time, i _C (k+1) is the DC bus capacitance current at k + 1 time, i _load (k) is the load current at time k, i _PV (k) is the power supply output current at time k, and C is DC bus capacitance, T _s is the sampling cycle time of the predictive controller, N is the number of bus voltage approach steps, V _dcref is the DC bus target voltage, V _dc (k) is the DC bus voltage at time k, i _ref ( k+1) is the output current of the hybrid energy storage system at time k+1; S22: According to the output current i _ref (k+1) of the hybrid energy storage system, obtain the predicted power of the hybrid energy storage system at time k+1, which is expressed as: p _ref (k+1)＝V _dcref ·i _ref (k+1) The predicted power is the power required to be stabilized by the hybrid energy storage system. 3. A hybrid energy storage collaborative control method based on model predictive control according to claim 2, characterized in that, in step S3, the predicted power _pref (k+1) is used as the outer loop power low-pass filter input, the output of the outer loop power low-pass filter p _bat (k+1) is the power demand of the energy storage battery; p _ref (k+1) minus the low-pass filter output is p _sc (k+1) which is the super Capacitor power requirements. 4. A hybrid energy storage collaborative control method based on model predictive control according to claim 3, characterized in that, in step S4, the reference values i _L1ref and i _L2ref of the energy storage battery and supercapacitor output current are calculated as : Among them, V _bat is the terminal voltage of the energy storage battery, and V _sc is the terminal voltage of the supercapacitor. 5. A hybrid energy storage collaborative control method based on model predictive control according to claim 1, characterized in that step S5 includes: S51: Determine the current-to-duty cycle transfer function G _{id_bat} of the hybrid storage system under the small-signal model, which is expressed as: Among them, D _bat is the duty cycle, i _bat is the actual output current of the battery, C is the supercapacitor size, R is the load size, L ₁ is the first inductance size of the battery, V _dc is the DC bus voltage, s and d are both given Value; S52: Adjust the parameters of the PI controller transfer function for inner loop current tracking. The PI controller transfer function G _pi is expressed as: Among them, K _P is the proportional coefficient, K _i is the integral coefficient, and K _P and K _i are parameters to be tuned; S53: Use the inner loop current reference input value as the transfer function G _{id_bat} and the inner loop controller composed of the transfer function G _pi of the PI controller to obtain the actual output current i _bat ; S54: According to the output of the inner loop controller, determine a set of switching modes that meet the outer loop power demand and the inner loop current demand. 6. A hybrid energy storage collaborative control method based on model predictive control according to claim 1, characterized in that first the output power in all switching modes of the next sampling period is calculated, and then the next time is calculated based on the obtained output power. The output current of the energy storage battery and supercapacitor is sampled during the sampling period, and finally a group of switching modes closest to the power demand and current demand are selected and output to the switching tube of the main circuit of the energy storage battery or the main circuit of the supercapacitor to achieve hybrid energy storage for each group. The system performs MPC-PI control. 7. A hybrid energy storage collaborative control system based on model predictive control, characterized in that the system includes: an initial determination module for determining the DC microgrid structure of the hybrid energy storage system, and the hybrid energy storage system includes a parallel energy storage batteries and supercapacitors; The power prediction module is used to predict the predicted power required to be stabilized by the hybrid energy storage system at the next moment by using the outer loop model controller; The power prediction module is also used to divide the predicted power through an outer loop low-pass filter to obtain the power requirements of the energy storage battery and supercapacitor; A current prediction module, configured to calculate the reference values i _L1ref and i _L2ref of the battery and supercapacitor output currents according to the power requirements of the energy storage battery and the supercapacitor, and use the current reference values i _L1ref and i _L2ref as the battery and the inner loop current reference input value of the supercapacitor; A control module configured to input the inner loop current reference input value into the inner loop controller of the battery and supercapacitor, and determine a set of switches that meet the outer loop power demand and the inner loop current demand based on the output of the inner loop controller. mode to control the hybrid energy storage system.