Micro-grid controller and method for connecting multiple electric automobile power springs

Abstract

The invention provides a micro-grid controller and method for multi-electric-vehicle electric power spring access, the controller and method can be used for hierarchical cooperative control of an alternating-current micro-grid, and an upper-layer controller realizes power cooperative distribution among multi-electric-power-spring electric vehicles through a consistency algorithm based on a directed graph and a droop control strategy. The lower-layer controller adopts model-free adaptive constraint control with anti-saturation compensation to realize accurate voltage tracking, and voltage oscillation caused by inverter saturation can be effectively suppressed through a relative output observer based on a pseudo-partial derivative and a compensation mechanism based on input constraint, so that the electric energy quality of the micro-grid is improved, and the power consumption of the micro-grid is reduced. According to the invention, when the multi-power-spring electric vehicle is connected to the AC micro-grid, the rapid stabilization of the bus voltage and the significant reduction of the harmonic content can be realized under the conditions of grid voltage fluctuation and load abrupt change.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid power quality control, and specifically to a microgrid controller and method with multiple electric vehicle power springs connected thereto, which are applicable to voltage fluctuation suppression and power distribution in a scenario with a high proportion of new energy access. Background Art

[0002] Electric vehicles are idle for most of the day. The vehicle-to-grid technology allows electric vehicles to transmit the electric energy stored in themselves back to the grid, enabling the batteries of a large number of idle electric vehicles to participate in the grid operation as a distributed energy storage system and improving the flexibility of the grid. The connection of a large number of electric vehicles to the microgrid will have a serious impact on the stability of the microgrid. Especially when key loads fluctuate, the microgrid is prone to large voltage fluctuations, affecting the power quality of the microgrid. To meet this demand, a demand-side management mode has been proposed. As a demand-side management technology that can achieve power generation following power consumption in an AC microgrid, the power spring has received extensive attention. Therefore, in order to improve the power quality of an AC microgrid with a large number of electric vehicles connected thereto, it is necessary to study an advanced control system that can quickly adjust the bus voltages to the standard values when the AC microgrid faces voltage fluctuations and various disturbances.

[0003] However, traditional control methods are difficult to obtain satisfactory control effects when facing a system such as a power spring that is nonlinear, multi-variable, and strongly coupled. Among a large number of existing control technologies, the performance of the controller depends on the accuracy of the mathematical model establishment, and the ability to resist parameter perturbation and the external environment is insufficient. Secondly, the adjustment ability of a single power spring is not enough to support the stability of the entire system. In the cooperative control method of a multi-power spring system, the droop control strategy realizes power distribution by adjusting the output voltage and frequency, but this method has problems such as uneven power distribution and voltage fluctuations in a multi-power spring system. In addition, due to the problem of actuator output saturation not being taken seriously enough, actuator output saturation will cause the output voltage of the power spring system to chatter, resulting in an increase in the voltage waveform distortion rate and a decrease in power quality. Therefore, proposing a cooperative control method that can achieve reasonable power distribution and rapid frequency adjustment without an accurate mathematical model has become a technical problem in the cooperative control of a multi-electric vehicle power spring system. Summary of the Invention

[0004] The main purpose of the present invention is to provide a microgrid controller and method with multiple electric vehicle power springs connected thereto, which can be used for hierarchical cooperative control of an AC microgrid to achieve cooperative control of reasonable power distribution and rapid frequency adjustment.

[0005] First, a control method for a microgrid with multiple electric vehicle power springs connected is proposed. The control method can be used for hierarchical cooperative control of an AC microgrid and is implemented through a controller. The controller includes a hierarchical control structure composed of an upper-layer cooperative controller and a lower-layer voltage controller. The control method includes the following steps: Establish a mathematical model of the electric vehicle power spring, and perform coordinate transformation to achieve power decoupling; Combine the droop control strategy and the directed Figure 1 consensus method to design the upper-layer controller to complete power distribution, including: combining the droop control and the leader consensus protocol based on a directed graph to design a cooperative mechanism for multiple electric vehicle power springs, so as to achieve reasonable power distribution of each electric vehicle power spring; the upper-layer cooperative controller is used to combine the droop control and the leader consensus protocol based on a directed graph to design a cooperative mechanism for multiple electric vehicle power springs, and establish the following distribution objective: Among them, and are the reference voltage and reference frequency of the leader respectively; is the number of electric vehicle power springs, i and j respectively represent the i-th and j-th electric vehicle power springs, and are the actual voltage value and actual frequency value respectively; and are the maximum rated output active power and reactive power of the electric vehicle power spring respectively, and are the actual power values; Based on the directed graph topology, construct a distributed consensus algorithm, set each electric vehicle power spring as an agent, and realize the coordinated regulation of the bus voltage and frequency and the dynamic power distribution of the electric vehicle with power springs through local information interaction to meet the distribution objective; Construct an equivalent dynamic linearized data model, introduce pseudo partial derivatives to describe the nonlinear influence; use an anti-saturation compensator and a relative output observer to design a model-free adaptive constraint control method to effectively solve the problem of power quality degradation caused by voltage oscillation caused by actuator saturation in the system; at the same time, design an output observer based on pseudo partial derivatives to improve the accurate tracking ability of the output voltage.

[0006] Furthermore, the upper-layer cooperative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower-layer voltage controller solves the voltage fluctuation problem and realizes accurate tracking of the output voltage; by setting the reference voltage of the leader, the reference frequency the maximum power and , and design the voltage, frequency, active power, and reactive power errors. Combine with the droop control strategy to obtain the d-axis and q-axis errors. Design the power spring control law to stabilize the critical load voltage.

[0007] Furthermore, the mathematical model of the electric vehicle power spring is: ; are the transmission line impedance, critical load impedance, and non-critical load impedance respectively; is the inductance value of the line impedance; C and L are the filter capacitor and inductor respectively; is the grid-side voltage, is the output voltage of the electric vehicle power spring, is the critical load voltage, is the inverter output voltage, are the injected current at the common node and the non-critical load current respectively, is the inverter output current; The discrete dynamic model of the electric vehicle power spring is: ; where, is the control input; is the system output; is the disturbance term; , , are the unknown orders of the system respectively; is the unknown dynamic function of the system, and k is the unit simulation step size; Adopt the compact form dynamic linearization method to simplify the electric vehicle power spring into an equivalent dynamic linearized data model. There is a pseudo partial derivative such that the following data model holds: , In the formula, and are the pseudo partial derivatives corresponding to the control input increment and the system output increment respectively, and is the control increment vector.

[0008] Furthermore, obtain the voltage and current injected at each common node. After coordinate transformation, convert from the stationary coordinate system of the single-phase system to the rotating coordinate system, and decouple to obtain the axis component; After adjustment by the voltage, frequency, active power, and reactive power errors, obtain the and axis components through coordinate transformation, where The axial component is the PWM control signal required by the converter.

[0009] Furthermore, after the upper-layer cooperative controller realizes cooperative control, it transmits the control signal to the lower-layer voltage controller; the lower-layer voltage controller adopts a model-free adaptive constraint control method, including an anti-saturation compensator and a relative output observer based on pseudo partial derivatives; Combined with the anti-saturation compensator and the relative output observer, the following update formula is used to estimate the pseudo partial derivative online: ; where , , is the observer gain, is the relative output estimation error, and are the penalty factor and the step factor of the pseudo partial derivative estimation respectively, and k is the unit simulation step size.

[0010] Furthermore, the control law is designed as: where represents the saturation function, and are the control law step factor and the weight factor respectively, k is the unit simulation step size, is the observer gain, is the relative output estimation error, is the constrained control input, is the unconstrained control input, and are the upper limit value and the lower limit value of the control input respectively, is the output estimation value, is the disturbance term, is the compensation signal of the anti-saturation compensator, is the weight factor of the compensation signal.

[0011] Furthermore, a microgrid controller with multiple electric vehicle power springs connected is proposed. The controller can be used for hierarchical cooperative control of an AC microgrid. The characteristics are as follows: the controller of the microgrid includes a hierarchical control structure composed of an upper-layer cooperative controller and a lower-layer voltage controller. Among them, the upper-layer cooperative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower-layer voltage controller solves the voltage fluctuation problem and realizes accurate tracking of the output voltage. The controller is used to implement the microgrid control method with multiple electric vehicle power springs connected.

[0012] Furthermore, a microgrid control device is proposed. The microgrid control device includes a processor, a memory, and a program of a microgrid control method for multi-electric vehicle power springs access stored on the memory and executable by the processor. When the program is executed by the processor, the steps of the microgrid control method for multi-electric vehicle power springs access are implemented.

[0013] Furthermore, a computer-readable storage medium is proposed. A program of a microgrid control method for multi-electric vehicle power springs access is stored on the computer-readable storage medium. When the program is executed by a processor, the steps of the microgrid control method for multi-electric vehicle power springs access are implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: A hierarchical cooperative control method and a controller for multi-electric vehicle power springs accessing an AC microgrid proposed in this application. First: The present invention does not require accurate model parameters. Through an equivalent data model based on compact-form dynamic linearization, a pseudo partial derivative online estimation and reset mechanism is introduced, and the control law is dynamically updated only relying on system input and output data, thereby realizing model-free adaptive control without designing the controller mechanism based on accurate model parameters.

[0015] Second: Aiming at the voltage oscillation problem caused by the saturation of the inverter duty ratio, an anti-saturation compensator is proposed. By dynamically compensating the signal and constraining the input amplitude with a saturation function, the over-limit control output is corrected in real time, effectively reducing the waveform distortion rate and harmonic content of the key load voltage, and improving the accuracy and stability of the voltage tracking of the electric vehicle power spring.

[0016] Third: The upper-layer controller combines a directed-graph-based consensus algorithm and droop control. Through local information interaction, the power is dynamically allocated according to the capacity. Based on the maximum rated power, the power output ratio of each electric vehicle power spring is ensured to be consistent, avoiding the uneven distribution problem of traditional droop control. And by the consensus algorithm, the reference values of voltage and frequency are dynamically adjusted to quickly stabilize and coordinately regulate the voltages of each key load in the face of grid voltage fluctuations, improving the power quality. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following-described drawings are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative efforts.

[0018] Figure 1 It is a schematic structural diagram of an electric vehicle power spring accessing an AC microgrid in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the model of an AC microgrid with multiple electric vehicle power springs connected in the embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the upper-layer cooperative controller in the embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of the lower-layer voltage controller in the embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of the flow of cooperative control of multiple electric vehicle power springs in the embodiment of the present invention. Detailed implementation manners

[0023] To make the purpose, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] Embodiment 1 A hierarchical cooperative control method for multiple electric vehicle power springs connected to an AC microgrid is proposed. Without an accurate dynamic model, it only performs online adaptive update through system input and output data; effectively compensates for the actuator saturation problem, improves the accuracy and stability of the voltage tracking of the electric vehicle power spring; realizes the cooperative control of multiple electric vehicle power springs through the upper-layer cooperative controller, effectively stabilizes the voltage of key loads, and improves the power quality.

[0025] The specific implementation steps are as follows: S1. Combine droop control with a leader-following consensus protocol based on a directed graph to design a cooperative mechanism for multiple electric vehicle power springs, thereby realizing reasonable power distribution among the electric vehicle power springs; S2. Adopt a data-driven model-free control method. For the nonlinear, multivariable, and strongly coupled characteristics of the system, construct an equivalent dynamic linearized data model through the discretized dynamic model of the electric vehicle power spring, and introduce a pseudo partial derivative to describe the unknown nonlinear influence of the system; S3. The model-free adaptive constraint control method is designed to effectively solve the problem of power quality degradation caused by voltage oscillation due to actuator saturation in the system. At the same time, an output observer based on pseudo partial derivative is designed to improve the accurate tracking ability of the output voltage. Among them, the stability of the bus voltage and frequency is crucial. To avoid the bus voltage drop caused by the line impedance, the specific distribution objective of the leader consensus protocol based on the directed graph in S1 is: Among them, the reference voltage of the leader and the reference frequency ; And are the actual voltage value and the actual frequency value respectively; The maximum power and are set; The actual voltage value and ; Design the voltage, frequency, active power, and reactive power errors: ; Among them, are the observation gains of voltage, frequency, active power, and reactive power respectively; are the observation errors of voltage, frequency, active power, and reactive power respectively; are the maximum rated output powers of the electric vehicle power springs respectively; S12. Construct a directed graph to describe the communication topology of multiple electric vehicle power springs. The node set represents N electric vehicle power springs. The adjacency matrix is defined as: If there is information transfer from the i-th to the j-th electric vehicle power spring, then , otherwise ; Establish the in-degree matrix and the Laplacian matrix to describe the network structure; If there is information transmission from the virtual leader to the i-th electric vehicle power spring, then , otherwise ; S13. Combine the droop control strategy to establish the d-axis error of the electric vehicle power spring: ; The q-axis error: ; Among them, are the relative errors of frequency and voltage respectively; After compensating the voltage, frequency, active power, and reactive power errors, and Axis component; among which The axis component serves as the optimal reference voltage for a single-phase AC system.

[0026] S21. The mathematical model of the electric vehicle power spring according to Figure 1 is as follows: .

[0027] Embodiment 2 According to Figure 2 The specific forms of the voltage and current injected into each common node are respectively , ; After coordinate transformation, the single-phase system is transformed from the stationary coordinate system to the rotating coordinate system, and the decoupled axis component and axis component ; It is decomposed into the d-axis component and the q-axis component through coordinate transformation .

[0028] Embodiment 3 The power injected into the common node according to Figure 3 can be expressed as: ; The specific form of the active power is established as: ; The specific form of the reactive power is established as: ; The mathematical model of the electric vehicle power spring in the d-axis and q-axis coordinates is established as: S22. The specific formula of the discretized dynamic model of the electric vehicle power spring in S1 is: ; Among them, is the control input; is the system output; is the disturbance term; are respectively the unknown orders of the system; is the unknown dynamic function of the system. Using the compact form dynamic linearization method, the electric vehicle power spring is simplified to an equivalent dynamic linearization data model, and there is a pseudo partial derivative such that the following data model holds: , , Among them, and are respectively the corresponding pseudo partial derivatives; the control input increment is ; the system output increment is: ; According to the equivalent linearization data model described in S22, the pseudo partial derivative estimation algorithm is as follows: ; S31. Update the estimated value of the pseudo partial derivative through the tracking error. Define the tracking error as: ; where is the desired system output; is the compensation signal for solving actuator saturation; S32. Design an anti-saturation compensator, and its compensation signal expression is: ; Among them, is the weight factor of the compensation signal; is the constrained control input; ensure that the control input of the controlled system satisfies the following amplitude input ; are respectively the lower limit and the upper limit of the control input; S33. Design a relative output observer, and its observer expression is; ; Among them, is the observer gain; is the output estimated value; define the relative output estimation error as ; further, we can get ; where ; further obtain the relative estimation error of the pseudo partial derivative ; according to the two-step delay estimation method, the approximate solution of is ; S34. Combine S32 and S33 to perform online estimation of the pseudo partial derivative using the following update formula; ; Establish a pseudo partial derivative reset mechanism: .

[0029] Example 4 Design the control law according to Figure 4 as: Among them, indicates that the saturation function is, and are the step factor and the penalty factor respectively.

[0030] Embodiment 5 As shown in the flowchart of the method in Figure 5, the coordinated control of multiple electric vehicle power springs can be realized, which specifically includes the following steps: Establish a mathematical model of the electric vehicle power spring; Perform coordinate transformation to achieve power decoupling; Combine the droop control strategy and the Figure 1 consensus method to design the upper-layer controller to complete power distribution; Construct an equivalent dynamic linearization data model and introduce pseudo partial derivatives to describe the nonlinear influence; Utilize the anti-saturation compensator and the relative output observer; Design the power spring control law to stabilize the key load voltage.

[0031] The above uses specific examples to elaborate on the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the technical field to which the present invention belongs, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made. It is easy for those skilled in the art to understand that the above are only the preferred embodiments of the present invention patent and are not intended to limit the present invention patent. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention patent shall be included in the protection scope of the present invention patent.

Claims

1. A control method for a microgrid with multiple electric vehicle power springs connected, which can be used for hierarchical collaborative control of an AC microgrid and is implemented through a controller, characterized in that: The controller includes a hierarchical control structure composed of an upper-layer cooperative controller and a lower-layer voltage controller, and the control method includes the steps of: Establish a mathematical model of the electric vehicle power spring, and perform coordinate transformation to achieve power decoupling; Design the upper-layer controller to complete power distribution by combining the droop control strategy and the directed graph consensus method, including: designing a multi-electric vehicle power spring cooperation mechanism by combining the droop control and the leader consensus protocol based on the directed graph, so as to realize the reasonable power distribution of each electric vehicle power spring; the upper-layer cooperative controller is used to design a multi-electric vehicle power spring cooperation mechanism by combining the droop control and the leader consensus protocol based on the directed graph, and establish the following distribution objective: Among them, and are the reference voltage and reference frequency of the leader respectively; is the number of electric vehicle power springs. i and j represent the i-th and j-th electric vehicle power springs respectively, and are the actual voltage value and actual frequency value respectively; and are the maximum rated output active power and reactive power of the electric vehicle power spring respectively, and are the actual power values; Construct a distributed consensus algorithm based on the directed graph topology, set each electric vehicle power spring as an agent, and achieve coordinated regulation of bus voltage and frequency and dynamic power distribution of electric vehicle power springs to meet the distribution objectives; Construct an equivalent dynamic linearization data model, and introduce pseudo partial derivatives to describe the nonlinear influence; use an anti-saturation compensator and a relative output observer to design a model-free adaptive constraint control method to effectively solve the power quality degradation caused by voltage chattering due to actuator saturation in the system; at the same time, design an output observer based on pseudo partial derivatives to improve the accurate tracking ability of the output voltage.

2. The microgrid control method for accessing multiple electric vehicle power springs according to claim 1, characterized in that: The upper-layer cooperative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower-layer voltage controller solves the voltage fluctuation problem and realizes accurate tracking of the output voltage; by setting the reference voltage of the leader , reference frequency , maximum power and , and designing the voltage, frequency, active power, and reactive power errors, combined with the droop control strategy, to obtain the d-axis and q-axis errors; designing the power spring control law to stabilize the critical load voltage.

3. A microgrid control method for accessing multiple electric vehicle power springs according to claim 1, characterized in that: The mathematical model of the electric vehicle power spring is: ； They are the transmission line impedance, the key load impedance, and the non-critical load impedance respectively; is the inductance value of the line impedance; C and L are the filter capacitor and inductor respectively; is the grid-side voltage, is the output voltage of the electric vehicle power spring, is the key load voltage, is the inverter output voltage, are the injection current at the common node and the non-critical load current respectively, is the inverter output current; The discrete dynamic model of the electric vehicle power spring is: ； Among them, is the control input; is the system output; is the disturbance term; , , are the unknown orders of the system respectively; is the unknown dynamic function of the system, and k is the unit simulation step size; Using the compact format dynamic linearization method, the electric vehicle power spring is simplified into an equivalent dynamic linearization data model, and there is a pseudo partial derivative such that the following data model holds: ， In the formula, and are the pseudo partial derivatives corresponding to the control input increment and the system output increment respectively, and is the control increment vector.

4. A microgrid control method for accessing a multi-electric vehicle power spring according to claim 1, characterized in that: The voltages and currents injected into each common node are obtained. After coordinate transformation, the single-phase system is converted from the stationary coordinate system to the rotating coordinate system, and the decoupled axis component is obtained; after adjustment by voltage, frequency, active power, and reactive power errors, the and axis components are obtained through coordinate transformation, where the axis component is the PWM control signal required by the converter.

5. A microgrid control method for accessing a multi-electric vehicle power spring according to claim 3, characterized in that: After the upper-layer cooperative controller realizes cooperative control, it transmits a control signal to the lower-layer voltage controller; the lower-layer voltage controller adopts a model-free adaptive constraint control method, including an anti-saturation compensator and a relative output observer based on pseudo partial derivatives; The following update formula is used to online estimate the pseudo partial derivative by combining the anti-saturation compensator and the relative output observer: ; Among them, , , are the observer gains, is the relative output estimation error, and are the penalty factor and the step factor of the pseudo partial derivative estimation respectively, and k is the unit simulation step size.

6. A microgrid control method for accessing multiple electric vehicle power springs according to claim 5, characterized in that: The control law is designed as: where represents the saturation function, and are the control law step factor and the weight factor respectively, k is the unit simulation step, is the observer gain, is the relative output estimation error, is the constrained control input, is the unconstrained control input, and are the upper limit value and the lower limit value of the control input respectively, is the output estimation value, is the disturbance term, is the compensation signal of the anti-saturation compensator, is the weight factor of the compensation signal.

7. A microgrid controller for accessing multiple electric vehicle power springs, which can be used for hierarchical cooperative control of an AC microgrid, characterized in that: The controller of the microgrid includes a hierarchical control structure composed of an upper-layer cooperative controller and a lower-layer voltage controller. Among them, the upper-layer cooperative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower-layer voltage controller solves the voltage fluctuation problem and realizes accurate tracking of the output voltage. The controller is used to implement the microgrid control method for multi-electric vehicle power spring access according to any one of claims 1-6.

8. A microgrid control device, characterized in that, The microgrid control device includes a processor, a memory, and a program of the microgrid control method for multi-electric vehicle power spring access stored on the memory and executable by the processor. When the program is executed by the processor, the steps of the microgrid control method for multi-electric vehicle power spring access according to any one of claims 1-6 are realized.

9. A computer-readable storage medium, characterized in that, A program of the microgrid control method for multi-electric vehicle power spring access is stored on the computer-readable storage medium. When the program is executed by the processor, the steps of the microgrid control method for multi-electric vehicle power spring access according to any one of claims 1-6 are realized.

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