CN116914901A - Hybrid energy storage cooperative control method and system based on model predictive control - Google Patents
Hybrid energy storage cooperative control method and system based on model predictive control Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J1/00—Circuit arrangements for dc mains or dc distribution networks
- H02J1/14—Balancing the load in a network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J15/00—Systems for storing electric energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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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
Technical Field
The invention relates to the technical field of hybrid energy storage system control, in particular to a hybrid energy storage cooperative control method and system based on model predictive control.
Background
In recent years, with the wide application of new energy power generation in electric power systems, the demand for efficient cooperative control of hybrid energy storage systems is becoming stronger. Typical hybrid energy storage systems consisting of high energy density energy storage cells and supercapacitors have difficulty meeting both the smoothness and rapidity of the hybrid system under the fluctuation of new energy output. Therefore, an effective cooperative control method is important for intermittent power stabilization of new energy and load demand change.
The traditional IP control is double closed-loop control for frequency division based on low-pass filter power, the control structure of the method is complex, the anti-interference performance is weakened, the parameter adjustment process is longer, and good control characteristics are difficult to maintain after the system working point changes. To overcome the conventional PI control drawbacks, sliding Mode Control (SMC) or distributed control may be employed. The sliding mode control has a variable structure and is insensitive to specific disturbance and parameter setting, but has the defects of communication delay, hysteresis, slow dynamic response and the like, and can introduce high-frequency buffeting, so that the improvement of the system frequency is limited. The distributed control is that the virtual resistor and the capacitor droop coefficient are used as a low-pass filter of an energy storage battery and a high-pass filter of a super capacitor during power generation and load change through a control strategy of dynamic power sharing among hybrid energy storage, but when the load is frequently switched, the direct-current micro-grid voltage based on droop control can be severely fluctuated, and the voltage drop caused by line impedance further influences the quality of the direct-current bus voltage.
Disclosure of Invention
In order to solve the problems, the invention provides a hybrid energy storage cooperative control method, a device, a terminal and a storage medium based on model predictive control.
The technical scheme of the invention is realized as follows:
a hybrid energy storage cooperative control method based on model predictive control, the method comprising:
s1: determining a direct current micro-grid structure of a hybrid energy storage system, wherein the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel;
s2: predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system by using an outer ring model controller;
s3: dividing the predicted power by an outer loop low-pass filter to obtain power requirements of a battery and a super capacitor;
s4: according to the power requirements of the energy storage battery and the super capacitor, calculating to obtain a reference value i of the output currents of the battery and the super capacitor Llref And i L2ref And comparing the current reference value i Llref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
s5: 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, so as to control the hybrid energy storage system.
Further, the method further comprises:
s21: according to a constant-speed approach mode of bus voltage recovery of a system super capacitor, calculating output current at the moment K+1 of a hybrid system, and expressing as follows:
where k is the sampling time, i C (k+1) is the DC bus capacitance current at time k+1, i load (k) For the load current at time k, i PV (k) The power supply outputs current at time k, C is a direct current bus capacitor, T s For the sampling period time of the predictive controller, N is the approach step number of the bus voltage, V dcref Is the target voltage of the direct current bus, 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 of the hybrid energy storage system ref (k+1) to obtainThe predicted power at time k+1 of the hybrid energy storage system 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 further comprises:
the predicted power p ref (k+1) as an outer loop power low pass filter input, the outer loop power low pass filter output p bat (k+1) is the power demand of the energy storage battery; p is p ref Subtracting the low pass filter output from (k+1) to obtain p sc (k+1) is the power requirement of the supercapacitor.
Further, the method further comprises:
calculating to obtain a reference value i of output currents of the energy storage battery and the super capacitor Llref And i L2ref The method comprises the following steps:
wherein V is bat For storing battery terminal voltage, V sc Is the terminal voltage of the super capacitor.
Further, the method further comprises:
s51: determining a transfer function G of current versus duty cycle for a hybrid memory system in a small signal model id_bat The expression is:
wherein D is bat Is duty cycle, i bat For the actual output current of the battery, C is the size of the super capacitor, R is the size of the load, L 1 For the first inductance of the battery, V dc Is a DC bus voltage, s and d are bothA given value;
s52: tuning parameters of a PI controller transfer function of inner loop current tracking, the PI controller transfer function G pi The expression is:
wherein K is P Is a proportionality coefficient, K i Is an integral coefficient, K P And K i Are all parameters to be set;
s53: taking the reference input value of the inner loop current as a transfer function G id_bat Transfer function G with PI controller pi The inner loop controller is configured to obtain the actual output current i bat ；
S54: 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.
Further, the method further comprises:
the output power of all the switch modes in the next sampling period is calculated, the output currents of the energy storage battery and the super capacitor in the next sampling period are calculated according to the obtained output power, and finally a group of switch modes closest to the power requirement and the current requirement are selected to be output to the switch tubes of the energy storage battery main circuit or the super capacitor main circuit so as to realize MPC-PI control on each group of hybrid energy storage systems.
The embodiment of the invention also provides a hybrid energy storage cooperative control system based on model predictive control, which comprises the following steps:
the initial determining module is used for determining a direct-current micro-grid structure of the hybrid energy storage system, and the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel;
the power prediction module is used for predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system by using an outer loop model controller;
the power prediction module is also used for dividing the predicted power through an outer loop low-pass filter to obtain the power requirements of the energy storage battery and the super capacitor;
the current prediction module is used for calculating and obtaining a reference value i of the output currents of the battery and the super capacitor according to the power requirements of the energy storage battery and the super capacitor L1ref And i L2ref And comparing the current reference value i L1ref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
and the control module is used for inputting the inner ring current reference input value into an inner ring controller of the battery and the super capacitor, and determining a group of switching modes meeting the outer ring power requirement and the inner ring current requirement according to the output of the inner ring controller so as to control the hybrid energy storage system.
The embodiment of the invention provides a hybrid energy storage cooperative control method based on model predictive control, which is used for determining a direct-current micro-grid structure of a hybrid energy storage system, wherein the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel; predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system 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; calculating to obtain reference values of output currents of the battery and the super capacitor according to the power requirements of the energy storage battery and the super capacitor, and taking the current reference values as inner ring current reference input values of the energy storage battery and the super capacitor of the system; inputting the inner loop current reference input value into the inner loop controller; 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, so as to control the hybrid energy storage system. Therefore, the invention can be based on the 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 the adjustment time.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart of a hybrid energy storage cooperative control method based on model predictive control in an embodiment of the invention;
FIG. 2 is a schematic diagram of a DC micro-grid topology of a hybrid energy storage system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a bus voltage recovery step according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an outer loop low pass filter control in an embodiment of the present invention;
FIG. 5 is a block diagram illustrating an energy storage cell current inner loop control in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a hybrid energy storage cooperative control system based on model predictive control in an embodiment of the invention;
FIG. 7 is a schematic diagram of simulation results of output power of a new energy photovoltaic power generation of a hybrid energy storage cooperative control method based on model predictive control in an embodiment of the invention;
FIG. 8 is a schematic diagram of simulation results of load power fluctuation of a hybrid energy storage cooperative control method based on model predictive control in an embodiment of the invention;
FIG. 9 is a schematic diagram of simulation results of hybrid energy storage power output prediction based on a hybrid energy storage cooperative control method of model predictive control in an embodiment of the invention;
FIG. 10 is a schematic diagram of simulation results of a hybrid energy storage high-low frequency power distribution based on a hybrid energy storage cooperative control method based on model predictive control in an embodiment of the invention;
FIG. 11 is a graph showing the effect of conventional PI dual-loop control added as a comparative experiment in the present invention;
FIG. 12 is a schematic of the MPC-PI control effect of the present invention added as a comparative experiment in an example of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to provide a clearer understanding of the technical features, objects and effects of the present invention. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present invention, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.
Referring to fig. 1, an embodiment of the present invention provides a hybrid energy storage cooperative control method based on model prediction control, which specifically includes the following steps:
s1: determining a direct current micro-grid structure of a hybrid energy storage system, wherein the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel;
s2: predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system by using an outer ring model controller;
s3: dividing the predicted power by an outer loop low-pass filter to obtain the power requirements of an energy storage battery and a super capacitor;
s4: according to the power requirements of the energy storage battery and the super capacitor, calculating to obtain a reference value i of the output currents of the battery and the super capacitor L1ref And i L2ref And comparing the current reference value i L1ref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
s5: 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, so as to control the hybrid energy storage system.
The method of the embodiment of the invention is executed by the terminal. The terminals may be various types of terminals; for example, the terminal may be, but is not limited to being, at least one of: a server, computer, tablet, or other electronic device.
Further, the step S1 includes:
s11: determining a direct-current micro-grid structure, wherein the direct-current micro-grid structure comprises a hybrid energy storage system formed by connecting an energy storage battery and a super capacitor in parallel, and the energy storage battery comprises a first inductor and a second inductor;
s12: according to the circuit mode corresponding to the switch in the direct current micro-grid structure, establishing a mathematical model of the hybrid energy storage system, wherein the mathematical model is as follows:
wherein L is 1 For the first inductance size, L 2 For the second inductance size, i L1 To flow through the first inductor current, i L2 For flowing the second inductance current, V BAT For storing the battery voltage, V sC For super capacitor voltage, V dc For system output voltage, SW 1 And SW 3 All are in a switching mode.
Exemplary, a dc micro-grid topology of a hybrid energy storage system as shown in fig. 2 includes a hybrid energy storage system with an energy storage battery and a super capacitor in parallel. L (L) 1 For the first inductance size, L 2 For the second inductance size, i L1 To flow through the first inductor current, i L2 For flowing the second inductance current, V BAT For storing the battery voltage, V SC For super capacitor voltage, V dc For system output voltage, SW 1 For the first switching mode SW 2 For the second switching mode SW 3 For the third switching mode SW 4 Is a fourth switch Guan Motai, and the switch mode takes a value of 0 or 1, SW 1 、SW 2 Value is opposite to SW 3 、SW 4 The values are opposite.
Further, the step S2 includes:
s21: according to a constant-speed approach mode of bus voltage recovery of a system super capacitor, calculating output current at the moment K+1 of a hybrid system, and expressing as follows:
where k is the sampling time, i C (k+1) is the DC bus capacitance current at time k+1, i load (k) For the load current at time k, i PV (k) The power supply outputs current at time k, C is a direct current bus capacitor, T s For the sample period time of the predictive controller,n is the approach step number of bus voltage, V dcref Is the target voltage of the direct current bus, 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 of the hybrid energy storage system ref (k+1) to obtain the predicted power at time k+1 of the hybrid energy storage system, expressed as:
p ref (k+1)＝V dcref ·i ref (k+1) (1.13)
wherein p is ref (k+1) is the predicted power at time k+1 of the system, V dcref Is the target voltage of the direct current bus, i ref (k+1) is the output current at time k+1 of the system; the predicted power is the power required to be stabilized by the hybrid energy storage system.
Further, the step S21 further includes:
s21a: according to a constant-speed approach mode of bus voltage recovery of the system super capacitor, a voltage predicted value of the super capacitor at the moment K+1 of the hybrid energy storage system is obtained, and the method is expressed as follows:
wherein N is the approach step number.
Specifically, the value of the approach step number N may be determined by setting the maximum steady-state tracking error:
exemplary, a bus voltage recovery step schematic, V, as shown in FIG. 3 dc (k) For the voltage value of the super capacitor end at sampling time k, V dc (k+1) is the voltage value recovered by the super capacitor end at time k+1, V dc (k+2) is the voltage value recovered by the super capacitor end at the moment k+2, V dcref For the target voltage value of the direct current bus recovered by the super capacitor terminal at the last moment, voltage recovery and the like are designed according to the bus voltage recovery step shown in fig. 3A fast approach mode.
S21b: current i flowing through bus capacitor C Can be expressed as:
wherein C is the size of the super capacitor, and u is the terminal voltage of the super capacitor.
S21c: the change of capacitance current can be deduced according to the change of bus voltage, and the discretization of the formula (0.4) can be obtained:
wherein i is C (k+1) is the current flowing through the super capacitor at time k+1 of the system, T s For the sampling period time of the predictive controller, V dc (k+1) is the terminal voltage of the super capacitor at the moment of the system k+1, V dc (k) The terminal voltage of the super capacitor at the moment of the system k.
S21d: according to the formulas (0.6) and (0.7), the direct-current bus capacitance current at the moment K+1 of the hybrid system is calculated, expressed as:
where k is the sampling time, i C (k+1) is the DC bus capacitance current at time k+1, i load (k) For the load current at time k, i PV (k) The power supply outputs current at time k, C is a direct current bus capacitor, T s For the sampling period time of the predictive controller, N is the approach step number of the bus voltage, V dcref Is the target voltage of the direct current bus, V dc (k) The voltage is the DC bus voltage at the moment k;
s21e: the new energy photovoltaic power generation adopts the maximum power point to track the power, and the output current of the battery at the sampling moment k is:
wherein V is PV_MPPT Corresponding voltage to the maximum power point of photovoltaic power generation, P PV (k) Tracking power for a maximum power point of the battery;
s21f: the output current of the hybrid energy storage system can be deduced according to kirchhoff's current law:
i ref ＝i c +i load -i PV (0.9)
wherein i is ref Output current for hybrid energy storage system, i load For the load current at the sampling instant i PV The battery output current at the sampling moment;
the kirchhoff current law is that the sum of currents flowing into any node in a circuit is equal to the sum of currents flowing out of the node.
S21g: the combination of (0.10), (0.11) and (1.11) can obtain the output current i of the hybrid energy storage system at the next moment ref (k+1), expressed as:
wherein i is ref (k+1) is the output current of the hybrid energy storage system at the moment k+1, i load (k) For the load current at sampling instant k, i PV (k) Sampling the battery output current at the moment k;
s21i: according to the output current i of the hybrid energy storage system ref (k+1) to obtain the predicted power p at the time of k+1 of the hybrid energy storage system ref (k+1)。
Therefore, the embodiment of the invention can design a prediction model based on the topological structure of the hybrid energy storage system based on the dynamic recovery capability of the bus voltage, and perform prediction control on the power output of the hybrid energy storage system, and ensure the stability of the bus voltage when the new energy output fluctuates and the load fluctuates, thereby improving the running stability of the whole system. And the power output of the hybrid energy storage system is predicted and controlled under the limitation of the reference quantity of the bus voltage, so that the bus can be ensured to reduce the overshoot as much as possible, the photovoltaic new energy is reasonably utilized, and the resources are saved.
Further, the step S3 includes:
s31: the predicted power p ref (k+1) as an outer loop power low pass filter input, the outer loop power low pass filter output p bat (k+1) is the power demand of the energy storage battery;
here, the outer loop power low pass filter transfer function can be expressed as:
wherein omega c Is the cut-off frequency; the predicted power allocation to the high frequency part and the low frequency part can be expressed as:
wherein P is ref Reference power, P for hybrid energy storage bat And P sc Respectively, the output power of the energy storage battery and the output power of the super capacitor.
S32：p ref Subtracting the low pass filter output from (k+1) to obtain p sc (k+1) is the power requirement of the supercapacitor.
Exemplary, an outer loop low pass filter control schematic as shown in FIG. 4, through which the power p is applied ref Frequency division decoupling to obtain high frequency power P sc And low frequency power P bat 。
Further, the step S3 further includes: according to formulas (1.3) and (1.13), the power requirements of the energy storage battery and the super capacitor are obtained at the next moment, expressed as:
P sc (k+1)＝p ref (k+1)-P bat (k+1) (0.16)
wherein p is bat (k+1) is the power requirement of the energy storage battery at the next time of the system, p sc (k+1) is the power requirement of the super capacitor at the next time of the system.
It can be appreciated that the filter has the characteristics of simple design and high response speed, and the cutoff frequency omega is used for ensuring that the energy storage battery can bear the electric quantity after equivalent low-pass filtering c The selection of (2) should be greater than or equal to the response time of the energy storage cell while ensuring as much as possible that the high frequency power fluctuations can be effectively separated. Therefore, the embodiment of the invention can decouple high-frequency power and low-frequency power through the low-pass filter, high-frequency power fluctuation is absorbed or provided by the super capacitor, low-frequency power fluctuation is absorbed or provided by the energy storage battery, and hybrid energy storage dynamic power sharing is realized.
Further, in the step S4, a reference value i of the output currents of the energy storage battery and the super capacitor is calculated Llref And i L2ref The method comprises the following steps:
wherein V is bat For storing battery terminal voltage, V sc Is the terminal voltage of the super capacitor.
Further, the step S5 includes:
s51: determining a transfer function G of current versus duty cycle for a hybrid memory system in a small signal model id_bat The expression is:
wherein D is bat Is duty cycle, i bat For the actual output current of the battery, C is the size of the super capacitor, R is the size of the load, L 1 For the first inductance of the battery, V dc For the direct current bus voltage, s and d are given values;
s52: tuning parameters of a PI controller transfer function for inner loop current tracking, the PI controlTransfer function G of a device pi The expression is:
exemplary, as shown in FIG. 5, the inner loop control block diagram of the energy storage battery current, the inner loop current tracking employs proportional-integral control, and the transfer function of the inner loop current low-pass filter includes a transfer function G of current versus duty cycle id_bat And PI controller G pi The PI controller G pi Can be expressed as:
wherein KP is a proportional coefficient, ki is an integral coefficient, and KP and Ki are parameters to be set;
s53: taking the reference input value of the inner loop current as a transfer function G id_bat Transfer function G with PI controller pi The inner loop controller is configured to obtain the actual output current i bat ；
S54: 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 and the mathematical model of the hybrid energy storage system.
Further, the step S51 further includes:
s51a: determining a system state equation of the bidirectional DC-DC circuit model of the energy storage battery, wherein the system state equation is expressed as follows:
wherein i is O For DC-DC actual output current, D bat Is a duty cycle;
s51b: according to the system state equation, a small signal model of the energy storage battery bidirectional DC-DC circuit model is established, and a transfer function G of current to duty ratio of the system under the small signal model is obtained id_bat 。
Further, the step S52 further includes: and setting parameters of a transfer function of the PI controller for tracking the inner loop current through a small signal model of the energy storage battery bidirectional DC-DC circuit model.
It will be appreciated that the current inner loop control block diagram and transfer function of the supercapacitor can be similarly obtained.
Further, the step S53 further includes: taking the deviation of the reference input value of the inner loop current and the actual output current as a transfer function G id_ba1 Transfer function G with PI controller pi The inner loop controller is configured to obtain the actual output current i bat 。
Further, the step S54 further includes:
s54a: based on the mathematical model of the hybrid energy storage system, namely the formula (0.21), an inner ring current output model of the hybrid energy storage system is obtained, and the method is expressed as follows:
s54b: predicting the current i from the inner loop Llref (k+1) and i L2ref (k+1) substituting the hybrid energy storage system inner loop current output model to determine a set of switching modes meeting the outer loop power demand and the inner loop current demand.
It can be understood that the model predictive control method provided by the invention specifically includes the steps of firstly calculating the output power of all the switch modes in the next sampling period, then calculating the output currents of the energy storage battery and the super capacitor in the next sampling period according to the obtained output power, and finally selecting a group of switch modes closest to the power requirement and the current requirement to output to the switch tubes of the energy storage battery main circuit or the super capacitor main circuit so as to realize MPC-PI control on each group of hybrid energy storage systems.
It should be noted that, the embodiment of the invention considers the influence of new energy output and load change on the voltage change of the direct current bus in the whole control process, and designs a double closed loop control strategy of the power outer loop and the current inner loop. Therefore, the embodiment of the invention can be based on the double closed-loop control strategy of the power outer loop and the current inner loop, improves the control rapidity, avoids complex parameter setting, has stronger stability to disturbance and shortens the adjustment time compared with the traditional PI double closed loop.
Referring to fig. 6, the embodiment of the invention further provides a hybrid energy storage cooperative control system based on model prediction control, which comprises: an initial determination module 201, a power prediction module 202, a current prediction module 203, a control module 204; wherein,,
the initial determining module 201 is configured to determine a dc micro-grid structure of a hybrid energy storage system, where the hybrid energy storage system includes an energy storage battery and a super capacitor connected in parallel;
the power prediction module 202 is configured to apply an outer loop model controller to predict and obtain predicted power that needs to be stabilized at a next moment of the hybrid energy storage system;
the power prediction module 202 is also configured to divide the predicted power by an outer loop low-pass filter to obtain power requirements of the energy storage battery and the super capacitor;
the current prediction module 203 is configured to calculate a reference value i of output currents of the battery and the super capacitor according to the power requirements of the energy storage battery and the super capacitor Llref And i L2ref And comparing the current reference value i L1ref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
the control module 204 is configured to input the inner loop current reference input value to an inner loop controller of the battery and the supercapacitor, and determine a set of switching modes that satisfy the outer loop power requirement and the inner loop current requirement according to an output of the inner loop controller, so as to control the hybrid energy storage system.
In some embodiments, the method further comprises:
the power prediction module is used for calculating the output current of the hybrid system at the moment K+1 according to the constant-speed approach mode of the bus voltage recovery of the system super capacitor, and is expressed as follows:
where k is the sampling time, i C (k+1) is the DC bus capacitance current at time k+1, i load (k) For the load current at time k, i pV (k) The power supply outputs current at time k, C is a direct current bus capacitor, T s For the sampling period time of the predictive controller, N is the approach step number of the bus voltage, V dcref Is the target voltage of the direct current bus, 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 for outputting current i according to the hybrid energy storage system ref (k+1) to obtain the predicted power at time k+1 of the hybrid energy storage system, 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 comprises:
the power prediction module is used for predicting the power p ref (k+1) as an outer loop power low pass filter input, the outer loop power low pass filter output p bat (k+1) is the power demand of the energy storage battery; p is p ref Subtracting the low pass filter output from (k+1) to obtain p sc (k+1) is the power requirement of the supercapacitor.
In some embodiments, the method further comprises:
the power prediction module is used for calculating and obtaining reference values iL1ref and iL2ref of the output currents of the energy storage battery and the super capacitor as follows:
wherein V is bat For storing battery terminal voltage, V sc Is the terminal voltage of the super capacitor.
In some embodiments, the method further comprises:
the current prediction module is used for determining a transfer function G of current to duty ratio of the hybrid storage system under a small signal model id_bat The expression is:
wherein D is bat Is duty cycle, i bat For the actual output current of the battery, C is the size of the super capacitor, R is the size of the load, L 1 For the first inductance of the battery, V dc For the direct current bus voltage, s and d are given values;
the current prediction module is used for setting parameters of a PI controller transfer function of inner loop current tracking, and the PI controller transfer function G pi Expressed as.
Wherein K is P Is a proportionality coefficient, K i Is an integral coefficient, K P And K i Are all parameters to be set;
the current prediction module is used for taking the reference input value of the inner loop current as a transfer function G id_bat Transfer function G with PI controller pi The inner loop controller is configured to obtain the actual output current i bat ；
The current prediction module is used for 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.
As an example, the present invention compares the proposed method with other methods. Specific:
photovoltaic is used as new energy power input, and a constant power load is adopted to verify the effectiveness of a control strategy of the hybrid energy storage system control method (MPC-PI control method) based on model predictive control. The experimental results are shown in fig. 6, 7 and 8, and the reliability of the MPC-PI control method is verified.
Specifically, the photovoltaic control mode adopts maximum power point tracking, the illumination intensity is actually changed by simulation, and the load power is switched from initial 50W to 250W at 0.5 s. The circuit topology parameters are set as follows: v (V) bat =24 (V), rated capacity 50 (Ah), response time 0.2s, V Sc =15 (V), inductance L 1 ，L 2 All 0.2mH and the bus capacitance 3mF. Considering the response speed of the energy storage battery and the cut-off frequency omega of the self-capacity low-pass filter c =2 (Hz). Finally determining the current inner loop PI controller parameter of the energy storage battery as K through setting the controller parameter p ＝15，K i =50, supercapacitor current inner loop PI controller parameter K p ＝19.25，K i ＝100。
The simulation result of the output power of the new energy photovoltaic power generation is shown in fig. 7, and the output power, the input illumination intensity, the output voltage and the output current of the photovoltaic power generation are respectively from top to bottom in the figure. It can be seen from the figure that illumination variation was added to the simulation to simulate photovoltaic power output fluctuations due to weather and the like. Meanwhile, the simulation result of load power fluctuation is shown in fig. 8, the simulation result is the uncertainty of load power change, and the simulation result of hybrid energy storage power output prediction obtained through model prediction control is shown in fig. 9.
The reference power of the energy storage battery and the supercapacitor is obtained by decoupling a filter, and as shown in a simulation result of the high-low frequency power distribution of the hybrid energy storage shown in fig. 10, the response speed of the energy storage is slower due to the characteristic reasons when the system is started, and the power output change is slower. The super capacitor can quickly respond, and can timely absorb or support transient power when power fluctuates each time, so that the power demand of the energy storage battery is smoothed, and the voltage fluctuation of the bus is further reduced.
Simulation results based on PI double-loop control and bus voltage stability under traditional PI-PI control are shown in FIG. 11, wherein a solid line with circles represents tracking signals of a PI-PI control system, and a solid line without circles represents reference signals of the PI-PI control system. The simulation result based on the voltage stability of the direct current bus under MPC-PI control is shown in FIG. 12, wherein the solid circled line represents the tracking signal of the MPC-PI control system and the solid non-circled line represents the reference signal of the MPC-PI control system. Comparing fig. 11 and fig. 12, it can be seen that the rising time of the system under the control of the model prediction, that is, the MPC-PI control is short, the overshoot is small, and the bus voltage can still be stabilized under the reference voltage under the conditions of photovoltaic power generation and load fluctuation. The simulation example verifies that the hybrid energy storage system can quickly respond to conditions such as load change and the like while ensuring dynamic power sharing under an MPC-PI control strategy, bus voltage fluctuation is very small, and the stability of the system is obviously improved.
It should be noted that: the technical schemes described in the embodiments of the present invention may be arbitrarily combined without any collision.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. The hybrid energy storage cooperative control method based on model predictive control is characterized by comprising the following steps of:
s1: determining a direct current micro-grid structure of a hybrid energy storage system, wherein the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel;
s2: predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system by using an outer ring model controller;
s3: dividing the predicted power by an outer loop low-pass filter to obtain power requirements of a battery and a super capacitor;
s4: according to the power requirements of the energy storage battery and the super capacitor, calculating to obtain a reference value i of the output currents of the battery and the super capacitor L1ref And i L2ref And comparing the current reference value i L1ref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
s5: 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, so as to control the hybrid energy storage system.
2. The hybrid energy storage cooperative control method based on model predictive control as set forth in claim 1, wherein the step S2 includes:
s21: according to a constant-speed approach mode of bus voltage recovery of a system super capacitor, calculating output current at the moment K+1 of a hybrid system, and expressing as follows:
where k is the sampling time, i C (k+1) is the DC bus capacitance current at time k+1, i load (k) For the load current at time k, i PV (k) The power supply outputs current at time k, C is a direct current bus capacitor, T s For the sampling period time of the predictive controller, N is the approach step number of the bus voltage, V dcref Is the target voltage of the direct current bus, 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 of the hybrid energy storage system ref (k+1) to obtain the predicted power at time k+1 of the hybrid energy storage system, 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. The hybrid energy storage cooperative control method based on model predictive control as set forth in claim 2, wherein in step S3, the predicted power p is calculated by ref (k+1) asAn outer loop power low pass filter input, an outer loop power low pass filter output p bat (k+1) is the power demand of the energy storage battery; p is p ref Subtracting the low pass filter output from (k+1) to obtain p sc (k+1) is the power requirement of the supercapacitor.
4. The hybrid energy storage cooperative control method based on model predictive control as claimed in claim 3, wherein in step S4, a reference value i of the output currents of the energy storage battery and the super capacitor is calculated L1ref And i L2ref The method comprises the following steps:
wherein V is bat For storing battery terminal voltage, V sc Is the terminal voltage of the super capacitor.
5. The hybrid energy storage cooperative control method based on model predictive control as set forth in claim 1, wherein step S5 includes:
s51: determining a transfer function G of current versus duty cycle for a hybrid memory system in a small signal model id_bat The expression is:
wherein D is bat Is duty cycle, i bat For the actual output current of the battery, C is the size of the super capacitor, R is the size of the load, L 1 For the first inductance of the battery, V dc For the direct current bus voltage, s and d are given values;
s52: tuning parameters of a PI controller transfer function of inner loop current tracking, thePI controller transfer function G pi The expression is:
wherein K is P Is a proportionality coefficient, K i Is an integral coefficient, K P And K i Are all parameters to be set;
s53: taking the reference input value of the inner loop current as a transfer function G id_bat Transfer function G with PI controller pi The inner loop controller is configured to obtain the actual output current i bat ；
S54: 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.
6. The hybrid energy storage cooperative control method based on model predictive control according to claim 1, wherein output power in all switching modes in a next sampling period is calculated first, then output currents of the energy storage battery and the super capacitor in the next sampling period are calculated according to the obtained output power, and finally a group of switching modes closest to power requirements and current requirements are selected to be output to a switching tube of the energy storage battery main circuit or the super capacitor main circuit so as to realize MPC-PI control on each group of hybrid energy storage systems.
7. A hybrid energy storage cooperative control system based on model predictive control, the system comprising: the initial determining module is used for determining a direct-current micro-grid structure of the hybrid energy storage system, and the hybrid energy storage system comprises an energy storage battery and a super capacitor which are connected in parallel;
the power prediction module is used for predicting and obtaining predicted power required to be stabilized at the next moment of the hybrid energy storage system by using an outer loop model controller;
the power prediction module is also used for dividing the predicted power through an outer loop low-pass filter to obtain the power requirements of the energy storage battery and the super capacitor;
the current prediction module is used for calculating and obtaining a reference value i of the output currents of the battery and the super capacitor according to the power requirements of the energy storage battery and the super capacitor L1ref And i L2ref And comparing the current reference value i L1ref And i L2ref As an inner loop current reference input value of the battery and the super capacitor;
and the control module is used for inputting the inner ring current reference input value into an inner ring controller of the battery and the super capacitor, and determining a group of switching modes meeting the outer ring power requirement and the inner ring current requirement according to the output of the inner ring controller so as to control the hybrid energy storage system.
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