Model prediction control method and device for two-stage coupling type battery energy storage grid-connected converter

Abstract

The invention relates to the technical field of energy storage system grid-connected control, and particularly discloses a two-stage coupling type battery energy storage grid-connected converter model prediction control method and device, and the method comprises the following steps: S1, constructing a two-stage coupling type battery energy storage grid-connected converter single-machine two-order composite model; s2, according to the single-machine dual-order composite model, constructing a prediction equation of the output current and the capacitor voltage of the inverter; s3, constructing a unified cost function of the output current and the capacitor voltage of the inverter; s4, predicting the switch state combination of all the switch tubes, and calculating the value of a corresponding unified cost function; and S5, selecting the switch state combination with the minimum uniform cost function value as the optimal switch state of all the switch tubes, and carrying out on-off control on the switch tubes. According to the invention, through unified modeling and control optimization, the dynamic response capability and the voltage stabilization performance of the energy storage grid-connected converter are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected control of energy storage systems, and in particular to a model predictive control method and device for a two-stage coupled battery energy storage grid-connected converter. Background Art

[0002] As the power grid continues to demand high-performance power quality management, battery energy storage systems (BESS) are gaining widespread attention as a key means of improving grid flexibility and stability. Traditional control strategies for power conversion systems (PCSs) typically employ hierarchical control of the inverter and DC / DC converter, making it difficult to achieve multi-objective coordinated optimization control. This is especially true when capacitor voltage fluctuates significantly, significantly reducing control effectiveness.

[0003] Most existing studies have improved the traditional grid-connected control methods of energy storage systems based on PI control, but all of them have problems such as slow dynamic response speed and difficulty in adjusting control parameters. In comparison, model predictive control is an advanced nonlinear controller with advantages such as fast dynamic response speed, no need for control parameter adjustment, and simultaneous multi-objective control. For example, the Chinese invention patent application publication number CN115459335A discloses an inverter model predictive control method for improving the stability of DC microgrids. The objective function is separated and each part of the objective function is sequentially evaluated to avoid the use of weighting factors, ultimately obtaining the optimal switch state. Existing methods often fail to comprehensively consider the coordinated optimization of the AC side output current and capacitor voltage in the control strategy, which limits the further improvement of the overall operating performance of the energy storage system.

[0004] The prior art has the following shortcomings and deficiencies: 1. Traditional control methods control the DC / DC converter and inverter separately, ignoring the dynamic coupling between the two; 2. The existing model predictive control method does not fully consider the control of DC bus capacitor voltage fluctuations; 3. The multi-objective coordinated control capability is insufficient, making it difficult to optimize the AC side current and battery capacitor voltage simultaneously. Summary of the Invention

[0005] The present invention aims to address the aforementioned issues. To this end, it provides a model predictive control method and apparatus for a two-stage coupled battery energy storage grid-connected converter. This innovative approach combines modeling of the bidirectional DC / DC converter and inverter modules within the energy storage system. Based on the model predictive control approach, a unified cost function is constructed that comprehensively considers the AC side current (inverter output current) and capacitor voltage, achieving multi-objective coordinated optimization control. Through unified modeling and control optimization, the present invention improves the dynamic response capability and voltage regulation performance of the energy storage grid-connected converter.

[0006] The present invention provides a model predictive control method for a two-stage coupled battery energy storage grid-connected converter, which adopts the following technical solution: comprising the following steps: S1: Construct a single-machine two-stage composite model of a two-stage coupled battery energy storage grid-connected converter; S2: Based on the single-machine two-order composite model, the prediction equations for the inverter output current and capacitor voltage are constructed; S3: Construct a unified cost function for the inverter output current and capacitor voltage; S4: Predict the switch state combinations of all switches and calculate the corresponding unified cost function values; S5: Select the switch state combination with the smallest unified cost function value as the optimal switch state of all switch tubes, and perform on-off control of the switch tubes.

[0007] Furthermore, the expression of the single-machine two-order composite model is: Among them, u dc is the capacitor voltage, t is the time, C dc The capacitor responsible for voltage stabilization on the DC side, d a is the duty cycle of the converter switch, i L is the output current of the energy storage battery, i out is the DC side current of the inverter, F p (t) is the voltage disturbance generated by the converter, L g is the AC inductance on the inverter side, i j are the currents of phase a, phase b, and phase c at the inverter output, j=a, b, c, u sj is the switching function of phase a, phase b, and phase c, L b F is the inductor in the bidirectional DC / DC converter connecting the energy storage side and the inverter side. q (t) is the current disturbance generated by the converter, u bat is the voltage of the energy storage battery, R g is the equivalent resistance on the inverter side, e j are the phase a, phase b, and phase c voltages on the grid side.

[0008] Furthermore, the prediction equation is expressed as: Where, represents the predicted value of the capacitor voltage at time k+1, represents the predicted value of the inverter output current at time k+1, represents the duty cycle of the converter switch at time k, Represents the sampled value of the energy storage battery output current at time k, represents the DC side current of the inverter at time k, represents the voltage disturbance generated by the converter at time k, represents the sampling value of the capacitor voltage at time k, represents the switching function of phases a, b, and c at time k, It indicates that the converter generates current disturbance at time k, represents the voltage of the energy storage battery at time k, represents the sampling value of the inverter output current at time k, represents the sampling period, Represents the phase a, phase b, and phase c voltages on the grid side at time k.

[0009] Furthermore, the unified cost function The expression is: Where, represents the first weight coefficient, represents the second weight coefficient, represents the absolute value function, Indicates the command value of the AC side current, Indicates the command value of the DC link capacitor voltage.

[0010] Furthermore, the switch state combination is a combination of on-off states of eight switch tubes that will not cause a dangerous state of short circuit or equipment damage.

[0011] Furthermore, the switch state combinations include (1,0,1,0,1,0,1,0), (0,1,1,0,1,0,1,0), (1,0,0,1,1,0,1,0), (1,0,1,0,0,1,1,0), (1,0,1,0,1,0,0,1), (0,1,0,1,1,0,1,0), (0,1,1,0,0,1,1,0), (0,1,1,0,0,1,1,0), (0,1,1,0,1,0,0,1), (1,0,0,1,0,1,1,0), (1,0,0,1,0,1,1,0). 0,1,1,0,0,1), (1,0,1,0,0,1,0,1), (0,1,0,1,0,1,1,0), (0,1,0,1,0,1,1,0), (0,1,0,1,1,0,0,1), (0,1,1,0,0,1,0,1), (1,0,0,1,0,1,0,1), (0,1,0,1,0,1,0,1), where "1" indicates that the switch tube is on and "0" indicates that the switch tube is off. The order of the corresponding switch tubes in each switching state combination is the switch tube on the inverter side and the switch tube on the converter side.

[0012] Furthermore, if the minimum unified cost function value corresponds to multiple switch state combinations, the switch state combination with the least switch tube action is selected as the optimal switch state of all switch tubes.

[0013] The present invention also provides a model predictive control device for a two-stage coupled battery energy storage grid-connected converter, which adopts the following technical solution: comprising: a sampling module, a prediction module and a switch tube control module, The sampling module is used to collect the inverter output current, capacitor voltage and energy storage battery output current of the battery energy storage grid-connected converter at the current moment; The prediction module is used to predict the switching state combination of all switching tubes based on the prediction equation according to the inverter output current, the capacitor voltage and the energy storage battery output current, calculate the value of the corresponding unified cost function, and select the switching state combination with the smallest unified cost function value as the optimal switching state of all switching tubes; The switch tube control module is used to control the on / off of the switch tube based on the optimal switch state.

[0014] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: 1. The present invention combines the DC / DC converter and the inverter into a two-stage model, fully considering the coupling effect between the two and improving the overall performance of the system control; 2. The unified cost function established by the present invention takes into account the fluctuations of AC side current and capacitor voltage, thus achieving multi-objective optimization control; 3. The present invention has the advantages of fast dynamic response and simple parameter setting, which improves the dynamic performance of the system; 4. The present invention effectively suppresses DC bus capacitor voltage fluctuations and improves system stability by optimizing the switch state in real time.

[0015] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of the method provided by the present invention.

[0018] Figure 2 It is a topological structure diagram of the single-machine two-stage composite model provided by the present invention.

[0019] Figure 3 It is a structural block diagram of the device provided by the present invention.

[0020] Reference numerals: 1. Sampling module; 2. Prediction module; 3. Switch tube control module. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0022] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0023] The following combination Figures 1 to 3 The present invention is further described in detail, and a model predictive control method and device for a two-stage coupled battery energy storage grid-connected converter of the present invention is described: In this embodiment, Figure 1 As shown, a model predictive control method for a two-stage coupled battery energy storage grid-connected converter is provided, comprising the following steps: S1: Construct a single-machine two-stage composite model of a two-stage coupled battery energy storage grid-connected converter.

[0024] The topology of the single-machine two-stage composite model is as follows: Figure 2 shown. Figure 2 C dc C is the capacitor responsible for voltage stabilization on the DC side. dc The left side is the bidirectional DC / DC converter part, and the right side is the AC / DC inverter part. bat is the voltage of the energy storage battery, L b The inductor in the bidirectional DC / DC converter connecting the energy storage side and the inverter side, i L Output current of the energy storage battery; S b1 is the first switch tube of the converter, S b2 It is the second switch tube of the converter. The on and off of the two are responsible for the mode switching of the converter. n1 -S n6 The six switching tubes on the inverter side; L g is the AC inductance on the inverter side, C g is the capacitor on the inverter side, and both of them play a filtering role; R g is the equivalent resistance on the inverter side, i out is the DC side current of the inverter, u dc is the capacitor voltage.

[0025] According to the topology, the mathematical model of the inverter side is derived as follows: Where U jo They represent the sum of the voltage of the filter part of the three-phase inverter a, b, and c phases and the voltage on the large grid side, respectively. j They represent the inverter output phase a, phase b, and phase c currents, respectively. j They represent the voltages of phase a, phase b, and phase c on the grid side respectively, j=a, b, c, and t represents time.

[0026] The energy storage battery performs step-up and step-down conversion through the DC / DC converter. Therefore, the mathematical state equation of the bidirectional converter side obtained by the state space averaging method is: Where, d a is the duty cycle of the converter switch tube, according to S b1 and S b2 The on-off state is determined by F p (t) is the voltage disturbance generated by the converter, F q (t) is the current disturbance generated by the converter.

[0027] The relationship between the sum of the voltage of the filter part of the three-phase inverter a, b, and c phases and the voltage on the large grid side and the capacitor voltage is: Where u sj Represent the switching functions of phase a, phase b, and phase c respectively. According to S n1 -S n6 The on / off state is determined.

[0028] Substituting the above formula into the mathematical model of the inverter side, we can get .

[0029] When the system is operating in steady state, , The capacitor voltage can be expressed as .

[0030] By coupling the AC / DC inverter and DC / DC converter in two stages, the expression of the single-machine two-stage composite model of the two-stage coupled battery energy storage grid-connected converter can be obtained as follows: .

[0031] S2: Based on the single-machine two-order composite model, the prediction equations of the inverter output current and capacitor voltage are constructed.

[0032] The control target is discretized based on the first-order Euler forward differential equation. By discretizing the single-machine two-order composite model of the two-stage coupled battery energy storage grid-connected converter, the prediction equations for the inverter output current and capacitor voltage are constructed. The expressions are as follows: Where, represents the predicted value of the capacitor voltage at time k+1, represents the predicted value of the inverter output current at time k+1, represents the duty cycle of the converter switch at time k, Represents the sampled value of the energy storage battery output current at time k, represents the DC side current of the inverter at time k, represents the voltage disturbance generated by the converter at time k, represents the sampling value of the capacitor voltage at time k, represents the switching function of phases a, b, and c at time k, It indicates that the converter generates current disturbance at time k, represents the voltage of the energy storage battery at time k, represents the sampling value of the inverter output current at time k, represents the sampling period, Represents the phase a, phase b, and phase c voltages on the grid side at time k.

[0033] S3: Construct a unified cost function for the inverter output current and capacitor voltage. This function comprehensively measures the tracking error of each control target (inverter output current and capacitor voltage), specifically: In order to simultaneously control the inverter output current and capacitor voltage, the errors of the two control objectives are fused into a scalar indicator, the control input is optimized, and a unified cost function considering the inverter output current and capacitor voltage is established. , the expression is Where, represents the first weight coefficient, represents the second weight coefficient, which is used to adjust the relative importance of the inverter output current error term and the capacitor voltage error term in the unified cost function. represents the absolute value function. Indicates the command value of the AC side current, The command value representing the DC side capacitor voltage can be obtained according to the state of each switching tube.

[0034] The unified cost function comprehensively considers the AC output current and capacitor voltage control on the AC / DC inverter side, which can minimize the capacitor voltage fluctuation while ensuring stable system operation.

[0035] S4: Predict the switching state combinations of all switching tubes and calculate the corresponding unified cost function values.

[0036] Based on the converter topology, all possible switch state combinations are listed. For example, each switch has two states (on or off), eliminating dangerous states that could cause short circuits or equipment damage. Ultimately, a limited set of candidate switch states is generated, each corresponding to a specific output voltage vector. These switch state combinations are combinations of eight switch on / off states that do not cause dangerous short circuits or equipment damage.

[0037] The switch state combinations include (1,0,1,0,1,0,1,0), (0,1,1,0,1,0,1,0), (1,0,0,1,1,0,1,0), (1,0,1,0,0,1,1,0), (1,0,1,0,1,0,0,1), (0,1,0,1,1,0,1,0), (0,1,1,0,0,1,1,0), (0,1,1,0,0,1,1,0), (0,1,1,0,1,0,0,1,0), (1,0,0,1,0,1,1,0, ,0,1,1,0,0,1),(1,0,1,0,0,1,0,1),(0,1,0,1,0,1,1,0),(0,1,0,1,1,0,0,1),(0,1,1,0,0,1,0,1),(1,0,0,1,0,1,0,1),(0,1,0,1,0,1,0,1),0,1,0,1,0,1), where “1” indicates that the switch is on and “0” indicates that the switch is off. The order of the corresponding switch tubes in each switch state combination is the switch tube S on the inverter side. n1 -S n6 and converter switch tube S b1 、S b2 .

[0038] Substitute each switch state combination into the unified cost function for prediction, and calculate the corresponding value of the unified cost function.

[0039] S5: Select the switch state combination with the smallest unified cost function value as the optimal switch state of all switch tubes, and perform on-off control of the switch tubes.

[0040] Compare the unified cost function values ​​corresponding to all possible switch state combinations and select the switch state combination that minimizes the unified cost function.

[0041] The switch state combination corresponding to the minimum unified cost function value is the switch state that the system should have at the next moment. The switch state combination vector is output by the real-time digital controller to each switch tube to control the conduction or shutdown of the switch tube.

[0042] If multiple states have the same cost, the one with the least switching actions is prioritized. That is, if the minimum uniform cost function value corresponds to multiple switch state combinations, the switch state combination with the least switching actions is selected as the optimal switch state for all switches. If there are multiple switch state combinations with the least switching actions, one is randomly selected as the optimal switch state.

[0043] This embodiment achieves unified and effective control of the DC / DC converter and inverter in the PCS, reduces the amount of prediction calculations, and simultaneously achieves rapid tracking of the AC side output current and capacitor voltage, improves the system's dynamic response capability, and effectively suppresses DC bus capacitor voltage fluctuations, thereby improving system stability.

[0044] In this embodiment, Figure 3 As shown, a model predictive control device for a two-stage coupled battery energy storage grid-connected converter is also provided, comprising: a sampling module 1, a prediction module 2 and a switch tube control module 3.

[0045] The sampling module is used to collect the inverter output current, capacitor voltage and energy storage battery output current of the battery energy storage grid-connected converter at the current moment; The prediction module is used to predict the switching state combination of all switching tubes based on the prediction equation according to the inverter output current, the capacitor voltage and the energy storage battery output current, calculate the value of the corresponding unified cost function, and select the switching state combination with the smallest unified cost function value as the optimal switching state of all switching tubes; The switch control module is used to control the on / off of the switch based on the optimal switch state. The real-time digital controller outputs a switch signal to each switch to control the on / off of each switch.

[0046] The execution entities of the above modules can be devices with computing functions such as computers, single-chip microcomputers, and microcontrollers. In specific implementation, the embodiments of the present invention do not limit the execution entities and they can be selected according to the needs of actual applications.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A model predictive control method for a two-stage coupled battery energy storage grid-connected converter, characterized in that: The following steps are involved: S1: Construct a single-machine two-stage composite model of a two-stage coupled battery energy storage grid-connected converter; S2: Based on the single-machine two-order composite model, the prediction equations for the inverter output current and capacitor voltage are constructed; S3: Construct a unified cost function for the inverter output current and capacitor voltage; S4: Predict the switch state combinations of all switches and calculate the corresponding unified cost function values; S5: Select the switch state combination with the smallest unified cost function value as the optimal switch state of all switch tubes, and perform on-off control of the switch tubes.

2. A model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to claim 1, characterized in that: The expression of the single-machine two-order composite model is: Among them, u dc is the capacitor voltage, t is the time, C dc The capacitor responsible for voltage stabilization on the DC side, d a is the duty cycle of the converter switch, i L is the output current of the energy storage battery, i out is the DC side current of the inverter, F p (t) is the voltage disturbance generated by the converter, L g is the AC inductance on the inverter side, i j are the currents of phase a, phase b, and phase c at the inverter output, j=a, b, c, u sj is the switching function of phase a, phase b, and phase c, L b F is the inductor in the bidirectional DC / DC converter connecting the energy storage side and the inverter side. q (t) is the current disturbance generated by the converter, u bat is the voltage of the energy storage battery, R g is the equivalent resistance on the inverter side, e j are the phase a, phase b, and phase c voltages on the grid side.

3. The model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to claim 2, wherein the prediction equation is expressed as follows: Where, represents the predicted value of the capacitor voltage at time k+1, represents the predicted value of the inverter output current at time k+1, represents the duty cycle of the converter switch at time k, represents the sampled value of the energy storage battery output current at time k, represents the DC side current of the inverter at time k, represents the voltage disturbance generated by the converter at time k, represents the sampling value of the capacitor voltage at time k, represents the switching function of phases a, b, and c at time k, It indicates that the converter generates current disturbance at time k, represents the voltage of the energy storage battery at time k, represents the sampling value of the inverter output current at time k, represents the sampling period, Represents the phase a, phase b, and phase c voltages on the grid side at time k.

4. A model predictive control method for a two-stage coupled battery energy storage grid-connected converter as claimed in claim 3, wherein the unified cost function The expression is: Where, represents the first weight coefficient, represents the second weight coefficient, represents the absolute value function, Indicates the command value of the AC side current, Indicates the command value of the DC link capacitor voltage.

5. A model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to claim 1, wherein the switch state combination is a combination of on-off states of eight switch tubes that will not cause a dangerous state of short circuit or equipment damage.

6. A model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to claim 5, wherein the switch state combinations include (1, 0, 1, 0, 1, 0, 1, 0), (0, 1, 1, 0, 1, 0), (1, 0, 0, 1, 1, 0, 1, 0), (1, 0, 1, 0, 0, 1, 1, 0), (1, 0, 1, 0, 1, 0, 1), (0, 1, 0, 1, 1, 0, 1, 0), (0, 1, 1, 0, 1, 0, 1, 0), (0, 1, 1, 0, 0, 1, 1, 0), (0,1,1,0,1,0,0,1), (1,0,0,1,0,1,1,0), (1,0,0,1,1,0,0,1), (1,0,1,0,0,1,0,1), (0,1,0,1,0,1,1,0), (0,1,0,1,1,0,0,1), (0,1,1,0,0,1,0,1), (1,0,0,1,0,1,0,1), (0,1,0,1,0,1,0,1), where, "1" indicates that the switch tube is turned on, and "0" indicates that the switch tube is turned off. The order of the corresponding switch tubes in each switch state combination is the switch tube on the inverter side and the switch tube on the converter side.

7. The model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to claim 1, wherein if the minimum unified cost function value corresponds to multiple switch state combinations, the switch state combination with the least switch tube action is selected as the optimal switch state for all switch tubes.

8. A model predictive control device for a two-stage coupled battery energy storage grid-connected converter, characterized in that: A method for executing a model predictive control method for a two-stage coupled battery energy storage grid-connected converter according to any one of claims 1 to 7, comprising: a sampling module, a prediction module, and a switch tube control module. The sampling module is used to collect the inverter output current, capacitor voltage and energy storage battery output current of the battery energy storage grid-connected converter at the current moment; The prediction module is used to predict the switching state combination of all switching tubes based on the prediction equation according to the inverter output current, the capacitor voltage and the energy storage battery output current, calculate the value of the corresponding unified cost function, and select the switching state combination with the smallest unified cost function value as the optimal switching state of all switching tubes; The switch tube control module is used to control the on / off of the switch tube based on the optimal switch state.

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