A robust optimization processing method for electric vehicle charging system based on event triggering

Abstract

The present invention discloses a robust optimization processing method for an electric vehicle charging system based on event triggering. The method firstly performs classified control design for non-ideal matching interference and non-matching interference based on the optimization control model established by the LQR method, and constructs a corresponding robust optimization strategy at the same time, and restricts the sampling state according to the event triggering control technology, that is, the charging data information is transmitted after a certain triggering condition is met. Compared with the traditional time triggering, the transmission cost is saved. The method is not only used to process the robustness problem of non-ideal systems, but also can optimize the robust performance.

Description

Technical Field

[0001] The invention relates to a control method for an electric vehicle charging power grid, and in particular to a robust optimization processing method for an electric vehicle charging system based on event triggering. Background Art

[0002] The electric vehicle charging system is a highly intelligent system with high precision, strong operation and strong coupling. The charging device is dispatched through power grid facilities, control centers, power stations and electric vehicle batteries. By coordinating the battery state of charge control, the uncertainty caused by the owner's behavior and the change of battery state of charge is taken into account.

[0003] The design methods applied to electric vehicle charging systems mainly include adaptive control and robust control. In order to coordinate the battery state of charge control, the owner's behavior and the uncertainty caused by the change of battery state of charge are considered to stabilize the charging system control and realize safety control, which causes risks and troubles. At the same time, some control algorithms are limited by certain disturbances, and there will be a lot of power waste and resource loss when dealing with interference. Therefore, it is necessary to use its experience knowledge, operation data, etc. to study new control algorithms related to robust optimization. Summary of the invention

[0004] The purpose of the present invention is to propose a robust optimization processing method for an electric vehicle charging system based on event triggering, which can effectively utilize its empirical knowledge and operation data to achieve good control effects when there are disturbances such as parameter perturbations, external interferences and unmodeled dynamics.

[0005] The specific technical solution of the present invention is as follows: A robust optimization processing method for an electric vehicle charging system based on event triggering comprises the following steps:

[0006] The robust optimization processing method of the smart grid system based on event triggering is characterized in that the control system model with different channel disturbances established by the method is:

[0007] ① Linear time-invariant system model with matching interference

[0008]

[0009] In the formula, x(t) is the relevant state vector obtained by monitoring sampling, u(t) is the control input, w(t) is the matching interference caused by various situations, and A m , B m , C m The system matrix obtained by establishing a mathematical model for the charging system,

[0010] ②Linear time-invariant system model with mismatched interference

[0011]

[0012] In the formula, x(t) is the relevant state vector obtained by monitoring sampling, u(t) is the control input, d(t) is the mismatch interference caused by various situations, and A nm , B nm , C nm The system matrix obtained by establishing a mathematical model for the charging system;

[0013]

[0014] Among them, load damping coefficient D; inertia coefficient M; speed regulator droop characteristic R g ; Speed ​​​​regulator T g ; Turbine time constant T t ; Electric vehicle droop characteristics pe; EVs gain K e ; Time constant T e ; Frequency offset constant b; Thermal turbine participation factor ag; Electric vehicle participation factor ae.

[0015] According to the control system model established above, the controller is designed. The specific steps are as follows:

[0016] ① For the linear time-invariant system model with matching interference

[0017] u(t)=u1(t)+u2(t)

[0018] in,

[0019]

[0020]

[0021] The relevant conditions are as follows:

[0022] (1)

[0023] (2) Q and R are secondary performance indicators The weight matrix of

[0024] (3)ψ(t)≥||w(t)||2;

[0025] (4)

[0026] ① For the linear time-invariant system model with mismatched interference

[0027]

[0028] The relevant conditions are as follows:

[0029] (1)

[0030] (2)

[0031] (3)β is the H∞ performance index.

[0032] Design the event trigger mechanism based on the system model established above:

[0033] ETM1:

[0034] ETM2:

[0035] Among them: φ1, φ2 are event trigger condition thresholds,

[0036] According to the event triggering mechanism provided, the effectiveness of the event-triggered approximate robust optimal controller is analyzed:

[0037] C001: Select the following form of Lyapunov energy function:

[0038] v(x)=x T Px

[0039] C002: Classify and analyze different forms of disturbances:

[0040] C003: State convergence analysis of systems with co-channel matching interference:

[0041]

[0042] C004: Decompose it into two terms to deal with the linear part and the nonlinear part (or interference term),

[0043]

[0044] C005: Where He(m) = m T +m and

[0045]

[0046] C006: where κ is a positive scalar and κ∈(0.5,1),

[0047] C007: Therefore, according to B004 and B005, the following formula can be obtained:

[0048]

[0049] C008: According to the Lyapunov energy function form in C001, the convergence of x(t) can be obtained.

[0050] C009: Based on the relevant proof of C007, the stability of the system with co-channel matching interference is obtained;

[0051] C010: State convergence analysis of systems with heterogeneous channel mismatched interference.

[0052] C011: Provide corresponding certification according to C010:

[0053]

[0054] C012: Using Schur's complement lemma, we can get the following LMI:

[0055]

[0056] C013: Among them:

[0057]

[0058]

[0059]

[0060] C014: Analysis of the stability of systems with event-triggered matching disturbances:

[0061]

[0062] C015: Analysis of the stability of systems subject to event-triggered mismatched disturbances:

[0063]

[0064] C016: The feasibility of the event triggering mechanism in accordance with C007 is demonstrated. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0066] Figure 2 This is a state response diagram of the first state of processing matching interference using the method proposed by the present invention in an embodiment;

[0067] Figure 3 The state response diagram of the second state of processing matching interference by the method proposed by the present invention is shown in the embodiment;

[0068] Figure 4This is a state response diagram of the third state of processing matching interference using the method proposed by the present invention in an embodiment;

[0069] Figure 5 This is a state response diagram of the fourth state of processing matching interference using the method proposed by the present invention in an embodiment;

[0070] Figure 6 This is a state response diagram of the fifth state of processing matching interference using the method proposed by the present invention in an embodiment;

[0071] Figure 7 This is a state response diagram of the first state of processing mismatched interference using the method proposed by the present invention in an embodiment;

[0072] Figure 8 This is a state response diagram of the second state of processing mismatched interference using the method proposed by the present invention in an embodiment;

[0073] Fig. 9 This is a state response diagram of the third state of processing mismatched interference using the method proposed by the present invention in an embodiment;

[0074] Fig.10 This is a state response diagram of the fourth state of processing mismatch interference using the method proposed by the present invention in an embodiment;

[0075] Fig.11 This is a state response diagram of the fifth state of processing mismatch interference using the method proposed by the present invention in an embodiment; DETAILED DESCRIPTION

[0076] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0077] like Figure 1 As shown, a robust optimization processing method for an electric vehicle charging system based on event triggering includes the following steps:

[0078] Step 1: Set various parameters;

[0079] Among them, load damping coefficient D = 0.0083; inertia coefficient M = 0.1667; speed regulator droop characteristic R g =2.4; speed regulator T g =0.08; turbine time constant T t =0.3; Electric vehicle droop characteristic pe = 1 / R g ;EVs gain K e =1; time constant T e=1; frequency offset constant b = 0.425; thermal turbine participation factor ag = 0.8; electric vehicle participation factor ae = 0.2

[0080] Step 2: Update the internal state. Based on the event trigger mechanism, update the real-time state that meets the event trigger conditions.

[0081] Step 3: If the internal state of the charging system is found to not meet the event triggering condition during the grid monitoring process, the last triggered state value will be transmitted.

[0082] Step 4: Repeat steps 2 and 3, and continue to operate after the charging system is stable;

[0083] Here is a real case:

[0084] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2-6 The state response diagram of the 1st to 5th states of matching interference processed by the method proposed by the present invention is shown in the embodiment; Figure 6-11 The state response diagram of the 1st to 5th states of the embodiment using the method proposed by the present invention to process mismatched interference;

[0085] It can be seen from the figure that the designed method can obtain relatively satisfactory control performance.

[0086] References

[0087] [1]Luo B, Huang T, Liu D.Periodic event-triggered suboptimal control with sampling period and performance analysis[J]. IEEE Transactions onCybernetics, 2019, 51(3): 1253-1261.

[0088] [2]Pham TN, Nahavandi S, Trinh H, et al.Static output feedbackfrequency stabilization of time-delay power systems with coordinated electricvehicles state of charge control[J]. IEEE Transactions on Power Systems, 2016, 32(5): 3862-3874.

Claims

1. A robust optimization processing method for an electric vehicle charging system based on event triggering, characterized in that: The following steps are involved: This method first establishes a control system model with different channel disturbances, as follows: ① Linear time-invariant system model with matching interference In the formula, x(t) is the relevant state vector obtained by monitoring sampling, u(t) is the control input, w(t) is the matching interference caused by various situations, and A m , B m , C m The system matrix obtained by establishing a mathematical model for the charging system, ②Linear time-invariant system model with mismatched interference In the formula, x(t) is the relevant state vector obtained by monitoring sampling, u(t) is the control input, d(t) is the mismatch interference caused by various situations, and A nm , B nm , C nm The system matrix obtained by establishing a mathematical model for the charging system; The robust optimal solution is sought through dynamic compensation, and a robust optimal controller for segmented processing of linear and nonlinear parts based on matching disturbance is designed. Conventional H∞ is used to suppress mismatched disturbance. The specific steps are as follows: u(t)=u1(t)+u2(t) in, The relevant conditions are as follows: Among them, Q and R are secondary performance indicators The weight matrix of ψ(t)≥||w(t)||2; For the linear time-invariant system model with mismatched disturbances: The relevant conditions are as follows: β is the H∞ performance index; The state is monitored and analyzed using an event-triggered device. Due to the mechanism of event-triggered control, this is defined as an approximate robust optimum. The effectiveness of the event-triggered robust optimal controller is analyzed to verify the effectiveness of the controller and the event-triggered device.

2. The method for robust optimization of an electric vehicle charging system based on event triggering according to claim 1 is characterized in that: The event trigger device is used to monitor and analyze the state. Due to the mechanism of event-triggered control, this is defined as an approximate robust optimality. The design of the event trigger mechanism is: in: is the event trigger condition threshold, 3. The method for robust optimization of an electric vehicle charging system based on event triggering according to claim 2 is characterized in that: The effectiveness of the event-triggered robust optimal controller is analyzed to verify the effectiveness of the controller and the event triggering device: B001: Select the following form of Lyapunov energy function: ν(x)=x T Px; B002: State convergence analysis of systems with co-channel matching interference; B003: Decompose it into two terms to deal with the linear part and the nonlinear part, B004: Among them, and Γ2=2x T PB m (u2+w) Where κ is a positive scalar and κ∈(0.5,1); Therefore, the following formula can be obtained: B005: According to B004, the stability of the system with co-channel matching interference is proved; B006: The proof of the state convergence analysis of a system with heterogeneous channel mismatched interference is as follows: B007: Using Schur's complement lemma, we can get the following LMI: middle: B008: Analysis of the stability of the system based on event-triggered matching interference: B009: Analysis of the stability of systems subject to event-triggered mismatched disturbances: B010: The feasibility of the event triggering mechanism has been demonstrated.

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