A microgrid controller and method for multi-electric vehicle power spring access

Abstract

The present invention proposes a microgrid controller and method for accessing multiple electric vehicle power springs. The controller and method can be used for hierarchical collaborative control of AC microgrids. The upper-level controller realizes power collaborative distribution among multiple electric vehicle power springs through a consistency algorithm based on a directed graph and a droop control strategy. The lower-level controller adopts model-free adaptive constraint control with anti-saturation compensation to achieve precise voltage tracking. The relative output observer based on pseudo-partial derivatives and the compensation mechanism based on input constraints can effectively suppress voltage oscillations caused by inverter saturation, thereby improving the power quality of the microgrid. The present invention enables the access of multiple electric vehicle power springs to the AC microgrid to achieve rapid stabilization of bus voltage and significant reduction of harmonic content under scenarios of grid voltage fluctuations and load mutations.

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 for multi-electric vehicle power spring access, which is suitable for voltage fluctuation suppression and power distribution in scenarios with a high proportion of new energy access. Background Art

[0002] Electric vehicles are idle for much of the day. Vehicle-to-grid technology allows electric vehicles to transfer their stored energy back to the grid, allowing the batteries of a large number of idle electric vehicles to participate in grid operations as distributed energy storage systems, thereby improving grid flexibility. The integration of a large number of electric vehicles into a microgrid can have a serious impact on the stability of the microgrid, especially when critical loads fluctuate. The microgrid is prone to large voltage fluctuations, which affect the power quality of the microgrid. To address this need, demand-side management models have been proposed. Power springs, as a demand-side management technology that enables AC microgrids to follow power generation, have attracted widespread attention. Therefore, to improve the power quality of AC microgrids connected to a large number of electric vehicles, the development of advanced control systems must ensure that the AC microgrid's bus voltages can quickly adjust to standard values ​​in the face of voltage fluctuations and various interferences.

[0003] However, traditional control methods struggle to achieve satisfactory control results for nonlinear, multivariable, and strongly coupled systems like electric springs. In many existing control technologies, controller performance relies on the accuracy of the mathematical model, resulting in insufficient resistance to parameter perturbations and external interference. Furthermore, the regulation capability of a single electric spring is insufficient to maintain the stability of the entire system. In collaborative control methods for multiple electric spring systems, droop control strategies achieve power distribution by adjusting output voltage and frequency. However, this approach suffers from problems such as uneven power distribution and voltage fluctuations in multiple electric spring systems. Furthermore, insufficient attention has been paid to the issue of actuator output saturation, which can cause the output voltage of the electric spring system to vibrate, resulting in an increase in voltage waveform distortion and a decrease in power quality. Therefore, developing a collaborative control method that can achieve reasonable power distribution and rapid frequency regulation without requiring a precise mathematical model has become a technical challenge for the collaborative control of multiple electric vehicle electric spring systems. Summary of the Invention

[0004] The main purpose of the present invention is to provide a microgrid controller and method for multi-electric vehicle power spring access, which can be used for hierarchical coordinated control of AC microgrids to achieve coordinated control of reasonable power distribution and rapid frequency regulation.

[0005] First, a microgrid control method for multi-electric vehicle power spring access is proposed. The control method can be used for hierarchical coordinated control of AC microgrids and is implemented by a controller. The controller includes a hierarchical control structure consisting of an upper-layer coordinated controller and a lower-layer voltage controller. The control method includes the following steps:

[0006] Establish a mathematical model of electric vehicle power springs and perform coordinate transformation to achieve power decoupling;

[0007] Combining droop control strategy and directed Figure 1 The upper-level controller is designed using a consistent approach to achieve power allocation, including: combining droop control with a directed graph-based leader consensus protocol to design a multi-EV power spring coordination mechanism, thereby achieving reasonable power distribution among the power springs of each EV; the upper-level coordination controller is used to combine droop control with a directed graph-based leader consensus protocol to design a multi-EV power spring coordination mechanism, establishing the following allocation objectives:

[0008]

[0009] in, 、 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 They are the actual voltage value and the actual frequency value respectively; and are the maximum rated output active power and reactive power of the electric vehicle power spring, and is the actual value of power;

[0010] Based on the directed graph topology, a distributed consensus algorithm is constructed, and each electric vehicle power spring is set as an intelligent agent. Through local information interaction, the bus voltage and frequency are coordinated and adjusted. Dynamic distribution of power of the electric spring electric vehicle to meet the distribution target;

[0011] An equivalent dynamic linearization data model is constructed, and pseudo-partial derivatives are introduced to describe nonlinear effects. A model-free adaptive constraint control method is designed using an anti-saturation compensator and a relative output observer to effectively address the power quality degradation caused by voltage jittering due to actuator saturation in the system. At the same time, an output observer based on pseudo-partial derivatives is designed to improve the precise tracking capability of the output voltage.

[0012] Furthermore, the upper collaborative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower 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 design the voltage, frequency, active power, and reactive power errors, combined with the droop control strategy to obtain the d-axis and q-axis errors; design the power spring control law to stabilize the key load voltage.

[0013] Furthermore, the mathematical model of the electric vehicle power spring is:

[0014] ;

[0015] 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, They are the common node injection current and non-critical load current, is the inverter output current;

[0016] The discretized dynamic model of the electric spring of an electric vehicle is:

[0017] ;

[0018] in, is the control input; is the system output; is the disturbance term; 、 、 are the unknown orders of the system; is the unknown dynamic function of the system, k is the unit simulation step;

[0019] The electric spring of electric vehicle is simplified into an equivalent dynamic linearization data model by using the compact dynamic linearization method. There is a pseudo partial derivative This makes the following data model valid:

[0020]

[0021] ,

[0022] Where, and The control input increments are and system output increment The corresponding pseudo partial derivative is, is the control increment vector.

[0023] Furthermore, the voltage and current injected into each common node are obtained. After coordinate transformation, the single-phase system is converted from a stationary coordinate system to a rotating coordinate system, and decoupling is obtained. Axis component; after adjusting the voltage, frequency, active power and reactive power errors, the coordinate transformation is obtained and Axis components, where The axis component is the PWM control signal required by the converter.

[0024] Furthermore, the upper-layer collaborative controller transmits a control signal to the lower-layer voltage controller after implementing collaborative control; the lower-layer voltage controller adopts a model-free adaptive constrained control method, including an anti-saturation compensator and a relative output observer based on pseudo partial derivatives;

[0025] The anti-saturation compensator and the relative output observer are combined to use the following update formula to estimate the pseudo partial derivative online: ;

[0026] in, , , is the observer gain, is the relative output estimation error, and are the penalty factor and step factor of the pseudo partial derivative estimation, respectively, and k is the unit simulation step size.

[0027] Furthermore, the designed control law is:

[0028]

[0029] in represents the saturation function, and are the control law step factor and weight factor respectively, k is the unit simulation step, is the observer gain, is the relative output estimation error, is the control input after constraint, is the unconstrained control input, and are the upper and lower limits of the control input, is the output estimate, is the disturbance term, is the compensation signal of the anti-saturation compensator, is the weight factor of the compensation signal.

[0030] Furthermore, a microgrid controller with multiple electric vehicle power springs connected is proposed. The controller can be used for hierarchical collaborative control of AC microgrids, and is characterized in that: the controller of the microgrid includes a hierarchical control structure consisting of an upper-level collaborative controller and a lower-level voltage controller, wherein the upper-level collaborative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower-level 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.

[0031] Furthermore, a microgrid control device is proposed, which includes a processor, a memory, and a program of a microgrid control method for multiple electric vehicle power spring access stored in the memory and executable by the processor, wherein when the program is executed by the processor, the steps of the microgrid control method for multiple electric vehicle power spring access are implemented.

[0032] Furthermore, a computer-readable storage medium is proposed, on which a program of a microgrid control method for multiple electric vehicle power spring access is stored. When the program is executed by a processor, the steps of the microgrid control method for multiple electric vehicle power spring access are implemented.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] This application proposes a hierarchical collaborative control method and controller for the access of multiple electric vehicle power springs to an AC microgrid. First, the present invention does not require precise model parameters. By introducing an equivalent data model based on tight-form dynamic linearization, an online estimation and reset mechanism of pseudo-partial derivatives is introduced, and the control law is dynamically updated only by relying on the system input and output data, thereby realizing model-free adaptive control without the need for precise model parameters for controller mechanism design.

[0035] Second: To address the voltage jitter problem caused by inverter duty cycle saturation, an anti-saturation compensator is proposed. By dynamically compensating the input amplitude with the saturation function constraint, the over-limit control output is corrected in real time, effectively reducing the waveform distortion rate and harmonic content of the critical load voltage, and improving the accuracy and stability of the electric spring voltage tracking of electric vehicles.

[0036] Third: The upper-level controller combines a directed graph-based consistency algorithm with droop control, and realizes dynamic power distribution according to capacity through local information interaction. It ensures the consistency of the power output ratio of each electric vehicle's electric spring based on the maximum rated power, avoiding the uneven distribution problem of traditional droop control. It also dynamically adjusts the reference values ​​of voltage and frequency through the consistency algorithm, quickly stabilizing and coordinating the voltages of key loads in the face of grid voltage fluctuations, thereby improving power quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below 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 paying any creative work.

[0038] Figure 1 This is a structural diagram of an electric vehicle power spring connected to an AC microgrid in an embodiment of the present invention.

[0039] Figure 2 Schematic diagram of a model of an AC microgrid connected to power springs of multiple electric vehicles in an embodiment of the present invention.

[0040] Figure 3 Schematic diagram of the upper-layer collaborative controller in an embodiment of the present invention.

[0041] Figure 4 Schematic diagram of the lower voltage controller in an embodiment of the present invention.

[0042] Figure 5 Schematic diagram of the process of coordinated control of electric springs of multiple electric vehicles in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the implementation 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 drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present invention, and should not be understood as limiting the present invention. Based on the embodiments in 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.

[0044] Example 1

[0045] A hierarchical collaborative control method for connecting multiple electric vehicle power springs to an AC microgrid is proposed. This method does not require a precise dynamic model and only performs online adaptive updates based on system input and output data. It effectively compensates for actuator saturation and improves the accuracy and stability of electric vehicle power spring voltage tracking. The upper-level collaborative controller is used to achieve collaborative control of multiple electric vehicle power springs, effectively stabilizing the voltage of critical loads and improving power quality.

[0046] The specific implementation steps are as follows:

[0047] S1. Combining droop control with a directed graph-based leader consensus protocol to design a multi-EV power spring coordination mechanism, thereby achieving reasonable power distribution among the EV power springs.

[0048] S2. Adopting a data-driven model-free control method, targeting the nonlinear, multivariable, and strongly coupled characteristics of the system, an equivalent dynamic linearization data model is constructed through the discretized dynamic model of the electric vehicle power spring, and pseudo partial derivatives are introduced to describe the unknown nonlinear effects of the system;

[0049] S3. A model-free adaptive constraint control method was designed to effectively address the power quality degradation caused by voltage chattering due to actuator saturation in the system. An output observer based on pseudo-partial derivatives was also designed to improve the accurate tracking capability of the output voltage.

[0050] The stability of bus voltage and frequency is crucial. To prevent bus voltage drops due to line impedance, the specific allocation goals of the directed graph-based leader consensus protocol in S1 are:

[0051]

[0052] Among them, the leader's reference voltage and reference frequency ; and The actual voltage value and actual frequency value respectively; set the maximum power and ; Actual voltage value and ;

[0053] Design voltage, frequency, active power and reactive power errors:

[0054] ;

[0055] in, They are voltage, frequency, active power and reactive power observation gains respectively; are the voltage, frequency, active power and reactive power observation errors respectively; They are the maximum rated output power of the electric spring of electric vehicles;

[0056] S12. Construct a directed graph Describe the communication topology of multiple EV power springs, node sets Represents N electric vehicle power springs, the adjacency matrix It is defined as: if there is information transmission from the i-th to the j-th electric vehicle power spring, then ,otherwise ; Establish the in-degree matrix and the Laplacian matrix Describe the network structure; if there is information transmission from the virtual leader to the electric spring of the i-th electric vehicle, then ,otherwise ;

[0057] S13. Combined with the droop control strategy, establish the d-axis error of the electric spring of the electric vehicle:

[0058] ;

[0059] Q-axis error: ;

[0060] in, are the relative errors of frequency and voltage respectively;

[0061] Regenerate after voltage, frequency, active power and reactive power error compensation and Axis components; where The axis component is used as the optimal reference voltage for single-phase AC systems.

[0062] S21. According to FIG1 , the mathematical model of the electric spring of an electric vehicle is:

[0063] .

[0064] Example 2

[0065] according to Figure 2 The specific forms of voltage and current injected into each common node are: , ;

[0066] After coordinate transformation, the single-phase system is transformed from the stationary coordinate system to the rotating coordinate system, and the decoupling is obtained Axis component and Axis component ;

[0067] Decomposed into d-axis component and q-axis component through coordinate transformation .

[0068] Example 3

[0069] According to Figure 3, the power injected into the public node can be expressed as:

[0070] ;

[0071] The specific form of establishing active power is:

[0072] ;

[0073] The specific form of establishing reactive power is:

[0074] ;

[0075] The mathematical model of the electric spring of the electric vehicle under the d-axis and q-axis coordinates is established as follows:

[0076]

[0077]

[0078]

[0079] S22. The specific formula of the discretized dynamic model of the electric vehicle power spring in S1 is:

[0080] ;

[0081] in, is the control input; is the system output; is the disturbance term; are the unknown orders of the system; is the unknown dynamic function of the system. Using the compact dynamic linearization method, the electric spring of the electric vehicle is simplified into an equivalent dynamic linearization data model. There is a pseudo partial derivative This makes the following data model valid:

[0082] ,

[0083] ,

[0084] in, and They are The corresponding pseudo partial derivative; the control input increment is ; The system output increment is: ;

[0085] According to the equivalent linearized data model described in S22, the pseudo partial derivative estimation algorithm is as follows:

[0086] ;

[0087] S31. Update the estimated value of the pseudo partial derivative by the tracking error, and define the tracking error as:

[0088] ;in is the desired system output; Compensation signal to solve actuator saturation;

[0089] S32. Design an anti-saturation compensator, whose compensation signal expression is:

[0090] ;

[0091] in, is the weight factor of the compensation signal; To constrain the control input; ensure that the control input of the controlled system meets the following amplitude input ; are the lower and upper limits of the control input respectively;

[0092] S33. Design a relative output observer, whose observer expression is:

[0093] ;

[0094] in, is the observer gain; is the output estimate; the relative output estimation error is defined as ; Further we can get ;in ; Further obtain the relative estimation error of pseudo partial derivative ; According to the two-step delay estimation method, The approximate solution is ;

[0095] S34, combining S32 and S33, uses the following update formula to perform online estimation of pseudo partial derivatives;

[0096] ;

[0097] Establish pseudo partial derivative reset mechanism:

[0098]

[0099] .

[0100] Example 4

[0101] According to Figure 4, the control law is designed as follows:

[0102]

[0103] in, The saturation function is, and are the step factor and penalty factor respectively.

[0104] Example 5

[0105] The flowchart of the method shown in FIG5 can realize the coordinated control of the electric springs of multiple electric vehicles, which specifically includes the following steps:

[0106] Establish a mathematical model of electric springs for electric vehicles;

[0107] Perform coordinate transformation to achieve power decoupling;

[0108] Combining droop control strategy and directed Figure 1 The upper controller is designed using a consistency method to complete power distribution;

[0109] Construct an equivalent dynamic linearized data model and introduce pseudo partial derivatives to describe nonlinear effects;

[0110] Using anti-windup compensator and relative output observer;

[0111] Design power spring control laws to stabilize critical load voltages.

[0112] The above uses specific examples to illustrate 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 art of the present invention, based on the idea of ​​the present invention, they can also make some simple deductions, deformations or replacements. It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A microgrid control method for multiple electric vehicle power spring access, which can be used for hierarchical coordinated control of AC microgrids and is implemented by a controller, characterized by: The controller includes a hierarchical control structure consisting of an upper-layer collaborative controller and a lower-layer voltage controller, and the control method includes the steps of: Establish a mathematical model of electric vehicle power springs and perform coordinate transformation to achieve power decoupling; The upper-level controller is designed to achieve power allocation by combining the droop control strategy and the directed graph consistency method. This includes: combining droop control with the directed graph-based leader consistency protocol to design a multi-electric vehicle power spring coordination mechanism, thereby achieving reasonable power distribution among the electric vehicle power springs; the upper-level coordination controller is used to combine droop control with the directed graph-based leader consistency protocol to design a multi-electric vehicle power spring coordination mechanism and establish the following allocation objectives: in, 、 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 They are the actual voltage value and the actual frequency value respectively; and are the maximum rated output active power and reactive power of the electric vehicle power spring, and is the actual value of power; Based on the directed graph topology, a distributed consensus algorithm is constructed, and each electric vehicle power spring is set as an intelligent agent. Through local information interaction, the bus voltage and frequency are coordinated and adjusted. Dynamic distribution of power of the electric spring electric vehicle to meet the distribution target; An equivalent dynamic linearization data model is constructed, and pseudo-partial derivatives are introduced to describe nonlinear effects. A model-free adaptive constraint control method is designed using an anti-saturation compensator and a relative output observer to effectively address the power quality degradation caused by voltage jittering due to actuator saturation in the system. At the same time, an output observer based on pseudo-partial derivatives is designed to improve the precise tracking capability of the output voltage.

2. A microgrid control method for multi-electric vehicle power spring access according to claim 1, characterized in that: The upper collaborative controller realizes efficient power distribution among multiple electric vehicle power springs, and the lower 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 design the voltage, frequency, active power, and reactive power errors, combined with the droop control strategy to obtain the d-axis and q-axis errors; design the power spring control law to stabilize the key load voltage.

3. The microgrid control method for multiple electric vehicle power spring access according to claim 1, characterized in that: 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, They are the common node injection current and non-critical load current, is the inverter output current; The discretized dynamic model of the electric spring of an electric vehicle is: ； in, 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, k is the unit simulation step; The electric spring of electric vehicle is simplified into an equivalent dynamic linearization data model by using the compact dynamic linearization method. There is a pseudo partial derivative This makes the following data model valid: ， Where, and The control input increments are and system output increment The corresponding pseudo partial derivative is, is the control increment vector.

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

5. The microgrid control method for multiple electric vehicle power spring access according to claim 3, characterized in that: The upper-layer collaborative controller transmits a control signal to the lower-layer voltage controller after implementing collaborative control; 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 anti-saturation compensator and the relative output observer are combined to use the following update formula to estimate the pseudo partial derivative online: ; in, , , is the observer gain, is the relative output estimation error, and are the penalty factor and step factor of the pseudo partial derivative estimation, respectively, and k is the unit simulation step size.

6. A microgrid control method for multiple electric vehicle power spring access according to claim 5, characterized in that: The designed control law is: in represents the saturation function, and are the control law step factor and weight factor respectively, k is the unit simulation step, is the observer gain, is the relative output estimation error, is the control input after constraint, is the unconstrained control input, and are the upper and lower limits of the control input, is the output estimate, 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 with spring-connected power for multiple electric vehicles, capable of hierarchical coordinated control of AC microgrids, characterized by: The controller of the microgrid includes a hierarchical control structure consisting of an upper-layer collaborative controller and a lower-layer voltage controller, wherein the upper-layer collaborative 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 accessing multiple electric vehicle power springs as described in 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 a microgrid control method for multiple electric vehicle power spring access stored on the memory and executable by the processor, wherein when the program is executed by the processor, the steps of the microgrid control method for multiple electric vehicle power spring access as described in any one of claims 1-6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program of a microgrid control method for multiple electric vehicle power spring access, wherein when the program is executed by a processor, the steps of the microgrid control method for multiple electric vehicle power spring access according to any one of claims 1 to 6 are implemented.

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