A mobile power supply guaranteeing system based on model prediction control and a parallel and off-grid seamless switching method

Abstract

The application discloses a mobile power supply guarantee system based on model prediction control and a parallel and off-grid seamless switching method, and comprises an interaction layer, a power layer, a control layer and an application layer. The application realizes rapid seamless switching within 10 ms, greatly improves response speed, controls voltage fluctuation in the switching process within 5%, limits current impact below 1.2 times of the rated value, improves the adaptability of the system to strong and weak power grids and various loads, eliminates control lag through prediction control, realizes multi-target coordinated optimization, and balances performance, smoothness and economy.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle and power grid interaction technology, specifically to a mobile power supply system based on model predictive control and a seamless switching method between on-grid and off-grid operation. Background Technology

[0002] Rapid and flexible power support in various emergency scenarios, such as major power supply disruptions and emergency disaster relief, is a crucial link in the reliability of the power grid and a key to safeguarding public health and meeting critical societal needs. Against the backdrop of building a new power system primarily based on new energy sources, fully exploring the diverse application potential of massive electric vehicles and maximizing their efficiency, using electric vehicles as energy storage media to replace conventional grid-side fixed energy storage, can effectively enhance the grid's flexible power supply support capabilities in emergency power supply scenarios. Simultaneously, it can address the problems of high safety risks, massive maintenance workload, and high initial investment associated with conventional grid-side fixed energy storage systems.

[0003] Mobile power supply systems, as crucial equipment for emergency power supply in power grids, directly impact power supply reliability and power quality through their grid-connected / off-grid switching performance. Currently, existing technologies primarily employ switching methods based on PI control and phase-locked loops (PLLs), such as the multi-machine parallel seamless grid-connected / off-grid switching system disclosed in invention patent CN105281367B, and the grid-connected / off-grid seamless switching energy storage system disclosed in invention patent CN118381089A; the "Microgrid Seamless Grid-Connected / Off-Grid Switching Strategy Based on Virtual Synchronous Machine" published in *Power Electronics Technology*, and the "Research on Microgrid Grid-Connected / Off-Grid Switching Strategy Based on Virtual Synchronous Machine" published in *Electrical Drive*, etc. However, these methods have the following shortcomings: (1) Slow response speed: Traditional PI control has inherent delay, and the switching time is usually 100-500ms, which cannot meet the fast switching requirements of high power supply scenarios.

[0004] (2) Large switching impact: The feedback-based control method generates a large voltage sag (30-50%) and current surge (2-3 times the rated value) at the moment of switching, which affects the normal operation of the load.

[0005] (3) Poor adaptability: Controllers with fixed parameters are difficult to adapt to complex operating conditions such as changes in strong and weak power grids and load fluctuations, and are prone to instability under weak power grid conditions.

[0006] (4) Lack of predictive ability: Traditional technology cannot predict the dynamic changes of the system and can only make corrections after the deviation occurs, resulting in a lag in control effect.

[0007] Definition: MPC controller (Model Predictive Control) is an advanced process control method that is widely used in industrial automation, robot control, energy management and other fields.

[0008] SVPWM generator: Space Vector Pulse Width Modulation Generator. Summary of the Invention

[0009] The present invention aims to provide a mobile power supply system based on model predictive control and a method for seamless switching between grid connection and off-grid operation.

[0010] To achieve the above objectives, the technical solution of the present invention is as follows: A method for seamless switching between grid connection and off-grid operation of a mobile power supply system based on model predictive control includes the following steps: Step 1: The energy storage device supplies power to the grid through a mobile power supply system; Step 2: The mobile power supply system detects the grid status and determines whether the grid status is normal. If it is normal, the mobile power supply system operates in grid-connected mode to supply energy to the grid; otherwise, it operates in off-grid mode to supply power to the user side at the back end of the grid. Step 3: The mobile power supply system monitors the power grid status in real time. If the power grid status remains unchanged, the mobile power supply system continues to operate in its original mode. If the power grid status changes, proceed to Step 4. Step 4: The MPC controller immediately takes action, predicts the system dynamics after grid disconnection, and solves for the optimal control variables at time k while satisfying the constraints. : (1) in, This represents the d-axis component of the inverter-side output voltage. The output voltage on the inverter side is represented by the q-axis component, and T represents the matrix transpose. The constraints include composite control variable constraints and state variable constraints. Step 5: The MPC controller continuously monitors the grid voltage and obtains the optimal control variables at each moment in real time, while meeting the constraints. Based on the optimal control variables at each time point The output voltage of the mobile power supply system is continuously optimized and adjusted to track the grid voltage. When the pre-synchronization conditions are met, the grid connection switch is closed, and the system switches from off-grid mode to grid-connected mode.

[0011] A further improvement is made to solve for the optimal control variables. The method is as follows: Using the d-axis and q-axis components of the grid voltage , Actual angular frequency of the power grid For the output variables of the MPC controller : (2) Mathematical model equations for the inverter and its filter circuit in a mobile power supply system: Inductor current equation: (3) in, , Inverter-side inductor current , Axial components; , Voltage of the inverter-side filter capacitor , Axial components; This refers to the virtual angular frequency. For inverter-side filter inductance; t is the equivalent resistance of the inductor; t is time. Capacitor voltage equation: (4) Where C is the value of the filter capacitor; , For grid current , Axial components; Based on the principles of virtual synchronous machines and circuit laws, the system differential equations are established as follows: (5) in: This is a reference value for mechanical power. To output active power to the inverter. ; This is a virtual moment of inertia; The damping coefficient; The rated angular frequency; This is a virtual work angle; This is the actual angular frequency of the power grid; Rearrange equations (1)-(5) into a state-space form, let: (6) (7) in, , These are the active and reactive power of the load, respectively. Represents the system state variable vector. Represents the perturbation input vector; (8) Represents a vector of nonlinear terms; Then there is Equations of state: (9) Output equation: (10) In equation (9), A represents the system state matrix, B represents the control input matrix, C represents the disturbance matrix, and D represents the direct transfer matrix. , Represents the system output vector; , From equations (2) and (9), based on the grid voltage, capacitor voltage, and line impedance, we obtain: (11) For the equivalent inductance of the line, The equivalent resistance of the line. , Voltage of the inverter-side filter capacitor , Axial component differentiation; Discretization: Let the sampling period be The discretized equation of equation (9) is: (12) In formula (11) (13) in, This represents the state vector at time k+1. , and For the coefficients of the uniformly discretized matrix, This represents the control input vector at time k. Let k represent the external perturbation vector at time k. Represents the vector of nonlinear terms after discretization; This represents the state vector at time k; Nonlinear terms in discretized equation (12) The linearization is achieved through Taylor expansion to meet the solution requirements of the MPC controller. Selecting the critical operating point for switching When switching from grid connection to off-grid The steady-state point before the power grid fault, during the transition from off-grid to on-grid. Pre-synchronization target point; For nonlinear terms exist Taylor expansion, ignoring higher-order infinitesimals: (14) This represents the steady-state value of the nonlinear function at the operating point. Represents a discrete nonlinear state function. Represents the partial derivative of the state vector x; Define the Jacobian matrix Substituting into the discretized equation (11), we obtain the linear discrete state equation: (15) make , The simplified form (15) is: (16) This represents the equivalent discrete state matrix after linearization. This represents the equivalent composite disturbance vector that combines external power grid disturbances with the constant offsets generated by nonlinear Taylor expansion. The MPC controller optimizes the control sequence by predicting the state and output of the mobile power supply system in the future N steps in the time domain, and derives the prediction expression based on the linearized equation (16): Set the prediction time domain as Control time domain is Control quantity in The value remains constant after each step. According to equation (16), we get to Predicted state values ​​for the step: (17) Combining equation (10), we obtain the output prediction vector. After sorting: (18) In formula (18): This represents the system's predicted output sequence vector for the next Np steps. This represents the predicted value of the system output at time k+1, given the known information at time k. For the predicted state matrix; The control matrix has dimensions of . ,Depend on and constitute; The perturbation matrix is ​​composed of stacked identity matrices; This represents the control input sequence vector within the future control time domain; Each sampling period Based on the current state and disturbance observation The weighted objective function is obtained by solving the quadratic programming algorithm while satisfying the constraints. smallest As the optimal control sequence : in, To track the error term, To track the smoothing term, To constrain penalty items; by First step control variables As the optimal control variable at time k .

[0012] Further improvements, To track error terms, the voltage, frequency, and phase tracking reference values ​​for the mobile power supply system are expressed as in equation (20): (20) Q is the diagonal weighted matrix of the output error. This represents the system output target reference value vector at time k+i in the future. When switching from off-grid to on-grid, the reference value is the grid parameter. When switching from on-grid to off-grid, the reference value is the rated parameter, which includes the rated phase voltage and rated frequency. The grid parameter includes voltage, frequency, and phase. i represents the prediction step number in the prediction time domain, with values ​​of 1, 2, ..., Np. To track the smoothing term, which is used to suppress abrupt changes in control input and reduce switching shocks, the expression is as shown in equation (21): (twenty one) R is a positive definite diagonal weighted matrix for control increments, the value of which is tuned based on the maximum allowable voltage change rate of the inverter switching transistors. This represents the vector of control input variables at time k+i in the future; The constraint penalty term is used to impose penalties on variables that exceed the limits, including inrush current and voltage fluctuations, as expressed in equation (22): (twenty two) The penalty coefficient is... These are the rated current and voltage of the mobile power supply system, and the corresponding inrush current and voltage fluctuation limits; Let be the absolute value of the predicted output voltage change at time k+i in the future. Let be the absolute value of the predicted output current at time k+i in the future. This indicates taking the larger value.

[0013] Further improvements include control variable constraints, as shown in equation (23): (twenty three) This represents the lower limit value of the control variable. Indicates the upper limit value of the control variable; State variable constraints, such as equations (24) and (25): current and voltage do not exceed limits; (twenty four) (25) in, This represents the absolute value of the inductor current predicted along the d-axis at time k+i in the future. This represents the absolute value of the inductor current predicted along the q-axis at time k+i in the future. This represents the predicted d-axis value of the capacitor voltage at time k+i. This represents the predicted q-axis value of the capacitor voltage at time k+i.

[0014] Further improvements include the following pre-synchronization conditions: voltage amplitude, phase, and frequency must be consistent before grid connection; (26) (27) in, To predict the system output angular frequency, To predict the system output phase, The upper limit of the allowable grid-connected voltage amplitude synchronization error, The upper limit of the allowable grid connection frequency synchronization error, This is the upper limit of the allowable grid-connected phase synchronization error.

[0015] A further improvement is that the energy storage device includes an electric vehicle and an energy storage cabinet.

[0016] A mobile power supply system based on model predictive control, used to run the above-mentioned mobile power supply system based on model predictive control and seamless switching between on-grid and off-grid, characterized in that it includes an interaction layer, a power layer, a control layer and an application layer. The application layer includes a cloud platform, an HMI local interaction module, and an RFID card reader module. The cloud platform is used for remote status monitoring, historical data storage, and global energy scheduling command issuance. The HMI local interaction module is used for visual display of on-site operating parameters, alarm query, and local operation and maintenance control. The RFID card reader module is used for on-site operator authentication and equipment start / stop permission management.

[0017] The control layer includes control board hardware, an MPC controller, and various communication interfaces. These interfaces include CAN, 485, 232, and Ethernet interfaces for connecting the internal BMS system and an external cloud platform, enabling transparent transmission of commands and status data. Voltage and current sampling interfaces are used to acquire the real-time closed-loop feedback status quantities required by the MPC controller. Temperature sampling interfaces, independent of the core algorithm, are used to monitor the temperature of power devices and trigger underlying hardware overheat derating protection. An emergency stop interface is used to instantly block inverter power generation through pure hardware logic in the event of a sudden hazard, ensuring the highest level of system safety. The power layer includes a main power line and a bidirectional AC / DC module; the main power line is used for power transmission, and the bidirectional AC / DC module is used for bidirectional high-efficiency conversion of AC and DC power, realizing seamless conversion between grid-connected and off-grid modes; The interaction layer includes AC and DC interfaces for the access and output of various energy devices, specifically: AC grid interface, diesel generator / inverter interface, electric vehicle DC charging / discharging interface, emergency vehicle DC charging / discharging interface, energy storage unit DC interface, and photovoltaic DC interface; their specific uses are as follows: AC grid interface: used to connect to the external power grid to achieve power interaction and synchronization in grid-connected mode; AC load interface: Used to independently supply power to local AC loads when offline; Diesel generator / inverter interface: used to connect an external diesel generator or a third-party inverter as a backup AC extension source for the system; Electric vehicle DC charging and discharging interface: used to connect electric vehicles and realize vehicle-to-grid (V2G) bidirectional charging and discharging; Emergency vehicle DC charging / discharging interface: used for rapid DC power exchange with external mobile emergency power vehicles; DC interface of energy storage unit: used to connect to internal or external battery clusters, serving as the basic carrier for system energy throughput; Photovoltaic DC interface: Used to connect to the photovoltaic array and work with the internal DC / DC module to achieve maximum power point tracking (MPPT) for solar power generation.

[0018] The beneficial effects of this invention are as follows: (1) Switching speed breaks through the bottleneck of traditional technology MPC predicts system dynamics in advance, eliminating the need to wait for deviations to occur, and inverter mode switching (current source → voltage source / converse) is completed in microseconds. Traditional phase-locked loop switching times are 100~500ms, while this invention, through MPC predictive control, achieves a switching time of ≤10ms (the lowest measured time is 8ms), meeting the "zero interruption" requirements of core loads such as power grid supply and data centers.

[0019] (2) Power quality has been greatly improved The constraint penalty term and the control smoothing term in the objective function work together to actively suppress electrical surges during switching. Voltage fluctuations are reduced from the traditional 30%~50% to ≤5%, and current surges are reduced from 2~3 times the rated value to ≤1.2 times, preventing downtime or damage to sensitive equipment.

[0020] (3) Significantly enhanced adaptability to complex working conditions In weak grid scenarios, disturbance compensation items are used. Real-time offsetting of grid impedance changes reduces the instability probability from over 30% with traditional technologies to zero. Load fluctuation scenarios: dynamic tracking. , Even with load changes of ±50%, the voltage frequency can still be kept stable.

[0021] (4) Multi-objective collaborative optimization, taking into account both performance and economy Replacing conventional stationary energy storage systems, this technology utilizes electric vehicles as the energy storage medium, reducing initial investment by 40% to 60% and maintenance workload by 70%. It enables flexible switching between multiple modes of "charging-powering-power-sustaining," increasing the duration of a single power-sustaining operation by 30%, and adapting to emergency, temporary, and routine needs. Attached Figure Description

[0022] Figure 1 A schematic diagram of a mobile power supply system connected to the power distribution network.

[0023] Figure 2 This is a system architecture diagram for a mobile supply guarantee system.

[0024] Figure 3 This is a block diagram of a mobile supply system.

[0025] Figure 4 Example diagram of application switching between on-grid and off-grid.

[0026] Figure 5 Flowchart of predictive control model for seamless switching between on-grid and off-grid environments.

[0027] Figure 6 This is a waveform diagram of the transition from grid connection to off-grid operation.

[0028] Figure 7 This is a waveform diagram of the off-grid to on-grid transition.

[0029] Figure 8 This is a waveform diagram under weak power grid conditions. Detailed Implementation

[0030] To make the purpose, technical solution and advantages of the invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and examples, but the embodiments of the invention are not limited thereto.

[0031] 1. Design of Mobile Supply and Protection System 1.1 Operation mode of mobile power supply and protection system connected to distribution network Mobile power supply and protection systems connected to the distribution network, such as Figure 1 As shown: (1) Rectification mode - DC charging: When the distribution network capacity is sufficient, the mobile power supply system can charge electric vehicles and energy storage systems from the grid to meet the charging needs of automobiles, energy storage equipment, etc. (2) Inverter mode: Grid connection: When the distribution network capacity is insufficient, electric vehicles / emergency vehicles, energy storage systems, and diesel generators output power to the distribution network through mobile power supply systems in a grid-connected manner to achieve temporary expansion of the distribution network and improve the stability of grid operation. Off-grid: When grid failures or temporary maintenance occur, electric vehicles / emergency vehicles, energy storage systems, and diesel generators can output power to the distribution network in an off-grid manner through mobile power supply systems, which can meet the needs of emergency power supply and temporary power supply. (3) The mobile power supply system also has the function of outputting power to the distribution network through inverters, photovoltaics, etc., in the form of grid-connected / off-grid mode.

[0032] 1.2 Design of Mobile Power Supply System The mobile power supply system is designed based on the principles of "AC / DC multi-energy interaction, multi-scenario collaborative control, and intelligent interactive management" and "layered and zoned control, and functional module decoupling." It constructs a four-layer system: "interaction layer—power layer—control layer—application layer," enhancing its capabilities for flexible AC / DC switching, multi-energy collaboration, and intelligent control. Figure 2 As shown.

[0033] Interaction layer: Includes various AC and DC interfaces for multi-functional input and output; Power layer: includes main power lines and bidirectional AC / DC modules; Control layer: includes control board hardware, algorithm software, and various communication interfaces; Application layer: includes local interaction and cloud platform / APP cloud management.

[0034] A block diagram of a mobile supply system is shown below. Figure 3 As shown, it includes AC / DC interfaces, various functional modules, input ports, communication ports, etc.

[0035] Seamless switching control algorithm design 2.1 The significance and content of on-grid and off-grid handover Instructions for switching between on-line and off-line networks, as described in section 6.1.1: Application Example 1 – Uninterrupted Power Supply Maintenance: For example… Figure 4As shown, when the upstream line L, which is normally supplying power to distribution box A, needs maintenance, the downstream (high) and low voltage user sides typically require a power outage. However, after connecting the mobile power supply backup system, when distribution box L is not disconnected, the mobile power supply backup system operates in grid-connected mode, supplying energy to the distribution network. When distribution box A is disconnected, the mobile power supply backup system automatically switches from grid-connected mode to off-grid mode, supplying power to the downstream (high) and low voltage user sides, ensuring the normal operation of important downstream loads. After maintenance is completed, distribution box A is closed, and the mobile power supply backup system automatically switches from off-grid mode to grid-connected mode. The entire process achieves uninterrupted maintenance.

[0036] Application Example 2 - Power Supply and Protection: The process is similar to Application Example 1. When a fault occurs on line L, the mobile power supply and protection system automatically switches from grid-connected mode to off-grid mode, that is, from power supply mode to power protection mode. When the fault on line L is restored, the mobile power supply and protection system automatically switches from off-grid mode to grid-connected mode, that is, from power protection mode to power supply mode.

[0037] Based on the above, the grid connection / off-grid switching response process is of decisive significance in the realization of functions such as uninterrupted power supply maintenance and power supply protection in mobile power supply and protection systems, as described in the background and purpose of the invention.

[0038] 2.2 Optimization Algorithm for Seamless Switching Between On-Grid and Off-Grid Microgrids Based on Model Predictive Control set up: , Inverter-side inductor current , Axial components; , Voltage of the inverter-side filter capacitor , Axial components; This refers to the virtual angular frequency. This is a virtual work angle; For inverter-side filter inductance; The equivalent resistance of an inductor; Using the d-axis and q-axis components of the inverter's output voltage on the inverter side as control variables: (1) Using the d-axis and q-axis components of the grid voltage , Actual angular frequency of the power grid For output variables: (2) , For grid current , Axial components; , The active and reactive power of the load; (1) First step, system model equations: Inductor current equation: (3) Capacitor voltage equation: (4) Based on the principles of virtual synchronous machines and circuit laws, the system differential equations are established as follows: (5) in: This is a reference value for mechanical power. To output active power to the inverter ( ); This is a virtual moment of inertia; The damping coefficient; The rated angular frequency; (2) Second step, organize the state equations according to the first step: Based on equations (1)-(5), we can rearrange them into a state-space form, letting (6) (7) (8) Then there is Equations of state: (9) Output equation: (10) ) In equation (9), , From (2) and (9), based on the grid voltage, capacitor voltage, and line impedance, we can obtain (11) For the equivalent inductance of the line, The equivalent resistance of the line. , This is the voltage derivative.

[0039] (3) The third step is discretization. Let the sampling period be According to equation (9), its discretization equation is: (12) In equation (12) (13) (4) Linear approximation Discretized equations containing nonlinear terms It needs to be linearized using Taylor expansion to meet the requirements of MPC solution: Selecting the critical operating point for switching (When switching from grid connection to off-grid, it is the steady-state point before the grid fault; when switching from off-grid to grid connection, it is the pre-synchronization target point.) For nonlinear terms exist Taylor expansion, ignoring higher-order infinitesimals: (14) Define the Jacobian matrix Substituting into the discretized equation (11), we obtain the linear discrete state equation: (15) make , (Perturbation term), simplified equation (15) is: (16) (5) Linear prediction model MPC optimizes the control sequence by predicting the system state and output N steps ahead (in the prediction time domain) and derives the prediction expression based on linearized equations: Set the prediction time domain as Control time domain is Control quantity in The value remains constant after each step. According to equation (16), it can be deduced that to Predicted state values ​​for the step: (17) Combining equation (10), the output prediction vector can be obtained: (18) In equation (17): For the predicted state matrix; For the control matrix, by and constitute; The perturbation matrix is ​​composed of stacked identity matrices.

[0040] (6) Objective function design (multi-objective coordinated optimization) To address the seamless switching requirement, a weighted objective function is designed that includes tracking error, control smoothness, and constraint penalties: (19) In equation (19), The tracking error term is expressed as in equation (20): to ensure that voltage, frequency and phase track the reference values ​​(tracking grid parameters when connected to the grid, and tracking rated parameters when disconnected from the grid), Q is the output error weighting matrix, and the voltage and frequency error weights are higher than those of other parameters; (20) The tracking smoothing term, expressed as in equation (21), suppresses abrupt changes in control input and reduces switching shocks. R is the control increment weighting matrix. (twenty one) The constraint penalty term, expressed as in equation (22), applies a penalty to variables that exceed limits (such as inrush current and voltage fluctuations). The penalty coefficient is... These are the rated current and voltage, respectively, and the corresponding inrush current and voltage fluctuation limits. (twenty two) (7) Setting constraints Control variable constraints: Inverter output voltage amplitude limit (based on hardware capabilities) (twenty three) State variable constraints: Current and voltage must not exceed limits. The predicted values ​​of the capacitor voltage on the d-axis and q-axis are... (twenty four) (25) Synchronization constraints (specifically for off-grid to grid-connected conversion): Before grid connection, voltage amplitude, phase, and frequency must be consistent. , , The allowable deviation for synchronization is typically 1% of the rated value. (26) (27) (8) Rolling optimization and solution Each sampling period Based on the current state and disturbance observation Solve the quadratic programming (QP) problem of equation (19) to obtain the optimal control sequence. ; Execute the first step of optimal control And control the output to the inverter; Update status in the next sampling period Repeat the above prediction, optimization, and execution process to achieve rolling optimization and offset the effects of dynamic changes and disturbances in the system.

[0041] Description of the seamless on-grid / off-grid handover control process: (1) Grid-connected to off-grid: When grid voltage loss is detected, the MPC controller immediately takes action. The predictive model can predict the system dynamics after grid disconnection and calculate the optimal PWM modulation wave in advance, enabling the inverter to smoothly transition from current source mode (grid-connected) to voltage source mode (off-grid) within microseconds, establishing a stable and accurate output voltage to ensure uninterrupted power supply to the load. The entire process is completed within 10ms, and the voltage drop is less than 5%.

[0042] (2) Off-grid to on-grid: When the system needs to be reconnected to the grid, the MPC controller continuously monitors the grid voltage. Under the constraint of the synchronization conditions of the objective function (consistent voltage amplitude, phase, and frequency), the optimizer dynamically and proactively adjusts the system output voltage through rolling optimization, enabling it to quickly and without overshoot track the grid voltage. When the pre-synchronization conditions are met, the grid connection switch is closed, and the inrush current is actively limited by the MPC to within 1.2 times the rated current.

[0043] The mobile power supply system of this invention was tested on a test platform with a rated power of 20kW and a voltage of 380V: like Figure 6 As shown, the grid-connected to off-grid switching time is 8.2ms, and the voltage drop is 4.1%. like Figure 7 As shown, the off-grid to on-grid switching time is 9.1ms, and the inrush current is 1.15 times the rated value.

[0044] like Figure 8 As shown, it still maintains stable operation under weak grid conditions (short-circuit ratio SCR=2).

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for seamless switching between grid connection and off-grid operation of a mobile power supply system based on model predictive control, characterized in that, Includes the following steps: Step 1: The energy storage device supplies power to the grid through a mobile power supply system; Step 2: The mobile power supply system detects the grid status and determines whether the grid status is normal. If it is normal, the mobile power supply system operates in grid-connected mode to supply energy to the grid; otherwise, it operates in off-grid mode to supply power to the user side at the back end of the grid. Step 3: The mobile power supply system monitors the power grid status in real time. If the power grid status remains unchanged, the mobile power supply system continues to operate in its original mode. If the power grid status changes, proceed to Step 4. Step 4: The MPC controller immediately takes action, predicts the system dynamics after grid disconnection, and solves for the optimal control variables at time k while satisfying the constraints. : （1） in, This represents the d-axis component of the inverter-side output voltage. The output voltage on the inverter side is represented by the q-axis component, and T represents the matrix transpose. The constraints include composite control variable constraints and state variable constraints. Step 5: The MPC controller continuously monitors the grid voltage and obtains the optimal control variables at each moment in real time, while meeting the constraints. Based on the optimal control variables at each time point The output voltage of the mobile power supply system is continuously optimized and adjusted to track the grid voltage. When the pre-synchronization conditions are met, the grid connection switch is closed, and the system switches from off-grid mode to grid-connected mode.

2. The seamless switching method for mobile power supply systems based on model predictive control as described in claim 1, characterized in that, Solving for the optimal control variables The method is as follows: Using the d-axis and q-axis components of the grid voltage , Actual angular frequency of the power grid For the output variables of the MPC controller : （2） Mathematical model equations for the inverter and its filter circuit in a mobile power supply system: Inductor current equation: （3） in, , Inverter-side inductor current , Axial components; , Voltage of the inverter-side filter capacitor , Axial components; This refers to the virtual angular frequency. For inverter-side filter inductance; t is the equivalent resistance of the inductor; t is time. Capacitor voltage equation: （4） Where C is the value of the filter capacitor; , For grid current , Axial components; Based on the principles of virtual synchronous machines and circuit laws, the system differential equations are established as follows: （5） in: This is a reference value for mechanical power. To output active power to the inverter, ; This is a virtual moment of inertia; The damping coefficient; This is the rated angular frequency; This is a virtual work angle; This is the actual angular frequency of the power grid; Rearrange equations (1)-(5) into a state-space form, let: （6） （7） in, , These are the active and reactive power of the load, respectively. Represents the system state variable vector. Represents the perturbation input vector; （8） Represents a vector of nonlinear terms; Then there is Equations of state: (9) Output equation: (10) In equation (9), A represents the system state matrix, B represents the control input matrix, C represents the disturbance matrix, and D represents the direct transfer matrix. , Represents the system output vector; , From equations (2) and (9), based on the grid voltage, capacitor voltage, and line impedance, we obtain: （11） For the equivalent inductance of the line, The equivalent resistance of the line. , Voltage of the inverter-side filter capacitor , Axial component differentiation; Discretization: Let the sampling period be The discretized equation of equation (9) is: （12） In formula (11) （13） in, This represents the state vector at time k+1. , and For the coefficients of the uniformly discretized matrix, This represents the control input vector at time k. Let represent the external perturbation vector at time k. Represents the vector of nonlinear terms after discretization; This represents the state vector at time k; Nonlinear terms in discretized equation (12) The linearization is achieved through Taylor expansion to meet the solution requirements of the MPC controller. Selecting the critical operating point for switching When switching from grid connection to off-grid The steady-state point before the power grid fault, during the transition from off-grid to on-grid. Pre-synchronization target point; For nonlinear terms exist Taylor expansion, ignoring higher-order infinitesimals: （14） This represents the steady-state value of the nonlinear function at the operating point. Represents a discrete nonlinear state function. Represents the partial derivative of the state vector x; Define the Jacobian matrix Substituting into the discretized equation (11), we obtain the linear discrete state equation: （15） make , The simplified form (15) is: （16） This represents the equivalent discrete state matrix after linearization. This represents the equivalent composite disturbance vector that combines external power grid disturbances with the constant offsets generated by nonlinear Taylor expansion. The MPC controller optimizes the control sequence by predicting the state and output of the mobile power supply system in the future N steps in the time domain, and derives the prediction expression based on the linearized equation (16): Set the prediction time domain as Control time domain is Control quantity in The value remains constant after each step. According to equation (16), we get to Predicted state values ​​for the step: （17） Combining equation (10), we obtain the output prediction vector. After sorting: （18） In formula (18): This represents the system's predicted output sequence vector for the next Np steps. This represents the predicted value of the system output at time k+1, given the known information at time k. For the predicted state matrix; The control matrix has dimensions of . ,Depend on and constitute; The perturbation matrix is ​​composed of stacked identity matrices; This represents the control input sequence vector within the future control time domain; Each sampling period Based on the current state and disturbance observation The weighted objective function is obtained by solving the quadratic programming algorithm while satisfying the constraints. smallest As the optimal control sequence : in, To track the error term, To track the smoothing term, To constrain penalty items; by First step control variables As the optimal control variable at time k .

3. The seamless switching method for mobile power supply systems based on model predictive control as described in claim 2, characterized in that, To track error terms, the voltage, frequency, and phase tracking reference values ​​for the mobile power supply system are expressed as in equation (20): （20） Q is the diagonal weighted matrix of the output error. This represents the system output target reference value vector at time k+i in the future. When switching from off-grid to on-grid, the target reference value is the grid parameter. When switching from on-grid to off-grid, the target reference value is the rated parameter, which includes the rated phase voltage and rated frequency. The grid parameter includes voltage, frequency, and phase. i represents the prediction step number in the prediction time domain, with values ​​of 1, 2, ..., Np. To track the smoothing term, which is used to suppress abrupt changes in control input and reduce switching shocks, the expression is as shown in equation (21): （21） R is a positive definite diagonal weighted matrix for control increments, the value of which is tuned based on the maximum allowable voltage change rate of the inverter switching transistors. This represents the vector of control input variables at time k+i in the future; The constraint penalty term is used to impose penalties on variables that exceed the limits, including inrush current and voltage fluctuations, as expressed in equation (22): （22） The penalty coefficient is... These are the rated current and voltage of the mobile power supply system, and the corresponding inrush current and voltage fluctuation limits; Let be the absolute value of the predicted output voltage change at time k+i in the future. Let be the absolute value of the predicted output current at time k+i in the future. This indicates taking the larger value.

4. The seamless switching method for mobile power supply systems based on model predictive control as described in claim 1, characterized in that, Control variable constraints, as shown in equation (23): （23） This represents the lower limit value of the control variable. Indicates the upper limit value of the control variable; State variable constraints, such as equations (24) and (25): current and voltage do not exceed limits; （24） （25） in, This represents the absolute value of the inductor current predicted along the d-axis at time k+i in the future. This represents the absolute value of the inductor current predicted along the q-axis at time k+i in the future. This represents the predicted d-axis value of the capacitor voltage at time k+i. This represents the predicted q-axis value of the capacitor voltage at time k+i.

5. The seamless switching method for mobile power supply systems based on model predictive control as described in claim 1, characterized in that, The pre-synchronization conditions are as follows: before grid connection, the voltage amplitude, phase, and frequency must be consistent; （26） （27） in, To predict the system output angular frequency, To predict the system output phase, The upper limit of the allowable grid-connected voltage amplitude synchronization error, The upper limit of the allowable grid connection frequency synchronization error, This is the upper limit of the allowable grid-connected phase synchronization error.

6. The seamless switching method for mobile power supply systems based on model predictive control as described in claim 1, characterized in that, The energy storage device includes an electric vehicle and an energy storage cabinet.

7. A mobile power supply system based on model predictive control, used for operating the mobile power supply system based on model predictive control as described in any one of claims 1-6, and a seamless on-grid handover method, characterized in that, It includes an interaction layer, a power layer, a control layer, and an application layer; The application layer includes a cloud platform, an HMI local interaction module, and an RFID card reader module. The cloud platform is used for remote status monitoring, historical data storage, and global energy scheduling command issuance. The HMI local interaction module is used for visual display of on-site operating parameters, alarm query, and local operation and maintenance control. The RFID card reader module is used for on-site operator authentication and equipment start / stop permission management. The control layer includes control board hardware, an MPC controller, and various communication interfaces. These interfaces include CAN, 485, 232, and Ethernet interfaces for connecting the internal BMS system and an external cloud platform, enabling transparent transmission of commands and status data. Voltage and current sampling interfaces are used to acquire the real-time closed-loop feedback status quantities required by the MPC controller. Temperature sampling interfaces, independent of the core algorithm, are used to monitor the temperature of power devices and trigger underlying hardware overheat derating protection. An emergency stop interface is used to instantly block inverter power generation through pure hardware logic in the event of a sudden hazard, ensuring the highest level of system safety. The power layer includes a main power line and a bidirectional AC / DC module; the main power line is used for power transmission, and the bidirectional AC / DC module is used for bidirectional high-efficiency conversion of AC and DC power, realizing seamless conversion between grid-connected and off-grid modes; The interaction layer includes AC and DC interfaces for the access and output of various energy devices, specifically: AC grid interface, diesel generator / inverter interface, electric vehicle DC charging / discharging interface, emergency vehicle DC charging / discharging interface, energy storage unit DC interface, and photovoltaic DC interface; their specific uses are as follows: AC grid interface: used to connect to the external power grid to achieve power interaction and synchronization in grid-connected mode; AC load interface: Used to independently supply power to local AC loads when offline; Diesel generator / inverter interface: used to connect an external diesel generator or a third-party inverter as a backup AC extension source for the system; Electric vehicle DC charging and discharging interface: used to connect electric vehicles and realize vehicle-to-grid (V2G) bidirectional charging and discharging; Emergency vehicle DC charging / discharging interface: used for rapid DC power exchange with external mobile emergency power vehicles; DC interface of energy storage unit: used to connect to internal or external battery clusters, serving as the basic carrier for system energy throughput; Photovoltaic DC interface: Used to connect to the photovoltaic array and work with the internal DC / DC module to achieve maximum power point tracking (MPPT) for solar power generation.

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