CN-122008935-A - Charging pile control parameter optimization method and device with power quality adjustment function

CN122008935ACN 122008935 ACN122008935 ACN 122008935ACN-122008935-A

Abstract

The invention relates to the technical field of grid-connected operation of charging piles, and particularly provides a charging pile control parameter optimization method and device with a power quality adjustment function, wherein the method comprises the steps of substituting electric operation parameters of a charging pile power grid side into a pre-constructed charging pile control parameter optimization model and solving to obtain an optimization result corresponding to the charging pile control parameters; the technical scheme provided by the invention can realize the dynamic self-adaptive adjustment of control parameters, thereby simultaneously taking account of reactive compensation precision, voltage stability and harmonic suppression effect in a complex power grid environment.

Inventors

LI ZHAOLIANG
WANG TONGXUN
ZHOU SHENGJUN
SHEN BING
ZHAO QIAN
LI FANGYI
YANG LIU
ZHANG PENG
LIU HAIJUN
MU XIAOBIN
LI WEIGUO
ZHAO GUOLIANG
WANG ZHIKAI

Assignees

中国电力科学研究院有限公司
国家电网有限公司
国网上海市电力公司

Dates

Publication Date: 20260512
Application Date: 20251204

Claims (14)

1. The utility model provides a charging pile control parameter optimization method that possesses electric energy quality regulatory function which characterized in that, the method includes: Substituting the electric operation parameters of the charging pile power grid side into a pre-constructed charging pile control parameter optimization model and solving to obtain an optimization result corresponding to the charging pile control parameters; The optimized result is issued to a bottom layer controller of the charging pile, and the operation parameters of the charging pile are dynamically adjusted; The optimization result comprises at least one of virtual inertia of the virtual synchronous generator, virtual damping in a virtual synchronous generator algorithm, reactive power loop controller proportion parameters, reactive power loop controller integral parameters, voltage controller proportion parameters, voltage controller integral parameters, n-order harmonic controller proportion parameters and n-order harmonic controller integral parameters.
2. The method of claim 1, wherein the pre-built charging pile control parameter optimization model comprises a multi-objective optimization function and corresponding constraints.
3. The method of claim 2, wherein the multi-objective optimization function is as follows: In the above formula, F is a multi-objective optimization function value, Q ref is a reactive power reference value, Q is a real-time monitored reactive power value, U ref is a voltage reference value, U is a real-time monitored voltage value, THD I is a total harmonic current distortion rate, and ω 1 、ω 2 、ω 3 is a weight coefficient dynamically adjusted according to real-time operation requirements of a power grid.
4. A method according to claim 3, wherein the dynamically adjusted weighting coefficients according to the real-time operating requirements of the grid are as follows: In the above description, ω 1_base 、ω 2_base 、ω 3_base is an initial preset first, second and third weight coefficient, K Q is a reactive weight adjustment coefficient, K PF is a power factor weight adjustment coefficient, K U is a voltage weight adjustment coefficient, K THD is a harmonic weight adjustment coefficient, Q set is a reactive instruction set value, Q m is a reactive actual measurement output value, U N is a rated voltage value, U m is a voltage actual measurement value, THD iN is a harmonic current standard limit value, THD im is a harmonic current distortion actual measurement value, PF m is a charging pile access point power factor actual measurement value, Φ PF is a power factor condition function, a function value is 1 when the conditions in brackets are satisfied, otherwise is 0, Φ U is a voltage condition function, a function value is 1 when the conditions in brackets are satisfied, otherwise is 0.
5. The method of claim 4, wherein the constraints are as follows: In the above formula, Δu is a control variable increment vector, Δu min is a control variable increment minimum value vector, Δu max is a control variable increment maximum value vector, u min is a control variable minimum value vector, u max is a control variable maximum value vector, y min is an output variable minimum value vector, y max is an output variable maximum value vector, x min is a state variable minimum value vector, x max is a state variable maximum value vector, u is a charging pile control parameter, x is an electric operation parameter variable on the charging pile grid side, and y is an output variable.
6. The method according to claim 5, wherein the electrical operating parameter variables of the grid side of the charging pile are as follows: The control parameters of the charging pile are as follows: The output variables are as follows: In the above formula, i d is the D-axis component of the ac side current of the grid-connected inverter, i q is the Q-axis component of the ac side current of the grid-connected inverter, U dc is the dc side capacitor voltage, δ is the virtual power angle in the virtual synchronous generator algorithm, ω is the virtual angular velocity in the virtual synchronous generator algorithm, J is the virtual inertia in the virtual synchronous generator algorithm, D is the virtual damping in the virtual synchronous generator algorithm, k pQ is the reactive power loop controller proportional parameter, k iQ is the reactive power loop controller integral parameter, k pU is the voltage controller proportional parameter, k iU is the voltage controller integral parameter, k phn is the n-order harmonic controller proportional parameter, k ihn is the n-order harmonic controller integral parameter, Q out is the real-time output reactive power of the inverter, U PCC is the inverter grid-connection point voltage amplitude, THD IPCC is the net side current total harmonic distortion rate, and T is the transposed symbol.
7. A charging pile control parameter optimizing device with a power quality adjusting function, characterized in that the device comprises: the analysis module is used for substituting the electric operation parameters of the charging pile power grid side into a pre-constructed charging pile control parameter optimization model and solving the model to obtain an optimization result corresponding to the charging pile control parameters; The adjusting module is used for transmitting the optimization result to a bottom layer controller of the charging pile and dynamically adjusting the operation parameters of the charging pile; The optimization result comprises at least one of virtual inertia of the virtual synchronous generator, virtual damping in a virtual synchronous generator algorithm, reactive power loop controller proportion parameters, reactive power loop controller integral parameters, voltage controller proportion parameters, voltage controller integral parameters, n-order harmonic controller proportion parameters and n-order harmonic controller integral parameters.
8. The apparatus of claim 7, wherein the pre-built charging pile control parameter optimization model comprises a multi-objective optimization function and corresponding constraints.
9. The apparatus of claim 8, wherein the multi-objective optimization function is as follows: In the above formula, F is a multi-objective optimization function value, Q ref is a reactive power reference value, Q is a real-time monitored reactive power value, U ref is a voltage reference value, U is a real-time monitored voltage value, THD I is a total harmonic current distortion rate, and ω 1 、ω 2 、ω 3 is a weight coefficient dynamically adjusted according to real-time operation requirements of a power grid.
10. The apparatus of claim 9, wherein the dynamically adjusted weighting coefficients according to grid real-time operating requirements are as follows: In the above description, ω 1_base 、ω 2_base 、ω 3_base is an initial preset first, second and third weight coefficient, K Q is a reactive weight adjustment coefficient, K PF is a power factor weight adjustment coefficient, K U is a voltage weight adjustment coefficient, K THD is a harmonic weight adjustment coefficient, Q set is a reactive instruction set value, Q m is a reactive actual measurement output value, U N is a rated voltage value, U m is a voltage actual measurement value, THD iN is a harmonic current standard limit value, THD im is a harmonic current distortion actual measurement value, PF m is a charging pile access point power factor actual measurement value, Φ PF is a power factor condition function, a function value is 1 when the conditions in brackets are satisfied, otherwise is 0, Φ U is a voltage condition function, a function value is 1 when the conditions in brackets are satisfied, otherwise is 0.
11. The apparatus of claim 10, wherein the constraints are as follows: In the above formula, Δu is a control variable increment vector, Δu min is a control variable increment minimum value vector, Δu max is a control variable increment maximum value vector, u min is a control variable minimum value vector, u max is a control variable maximum value vector, y min is an output variable minimum value vector, y max is an output variable maximum value vector, x min is a state variable minimum value vector, x max is a state variable maximum value vector, u is a charging pile control parameter, x is an electric operation parameter variable on the charging pile grid side, and y is an output variable.
12. The device according to claim 11, characterized in that the electrical operating parameter variables of the grid side of the charging pile are as follows: The control parameters of the charging pile are as follows: The output variables are as follows: In the above formula, i d is the D-axis component of the ac side current of the grid-connected inverter, i q is the Q-axis component of the ac side current of the grid-connected inverter, U dc is the dc side capacitor voltage, δ is the virtual power angle in the virtual synchronous generator algorithm, ω is the virtual angular velocity in the virtual synchronous generator algorithm, J is the virtual inertia in the virtual synchronous generator algorithm, D is the virtual damping in the virtual synchronous generator algorithm, k pQ is the reactive power loop controller proportional parameter, k iQ is the reactive power loop controller integral parameter, k pU is the voltage controller proportional parameter, k iU is the voltage controller integral parameter, k phn is the n-order harmonic controller proportional parameter, k ihn is the n-order harmonic controller integral parameter, Q out is the real-time output reactive power of the inverter, U PCC is the inverter grid-connection point voltage amplitude, THD IPCC is the net side current total harmonic distortion rate, and T is the transposed symbol.
13. A computer device, comprising: one or more processors; the processor is used for executing one or more programs; The method for optimizing control parameters of a charging pile with power quality adjustment function according to any one of claims 1 to 6 is implemented when the one or more programs are executed by the one or more processors.
14. A computer-readable storage medium, on which a computer program is stored, which, when executed, implements the method for optimizing control parameters of a charging pile with power quality adjustment function according to any one of claims 1 to 6.

Description

Charging pile control parameter optimization method and device with power quality adjustment function Technical Field The invention relates to the technical field of grid-connected operation of charging piles, in particular to a method and a device for optimizing control parameters of charging piles with a power quality adjusting function. Background With the rapid development of the electric automobile industry, the installed quantity and the single-machine power of the high-capacity direct-current charging pile serving as a core infrastructure are continuously increased. The random and intermittent access of the high-power loads forms a serious challenge for the power quality and the operation stability of the power distribution network. In order to solve the problem, the charging pile can be modified aiming at the control link, so that the charging pile has reactive power, voltage and harmonic regulation functions. Reactive power regulation can compensate reactive power shortage of a power grid, power factors are improved, VSG control provides necessary voltage support for the power grid by simulating rotor inertia and damping characteristics of a synchronous generator, system stability is enhanced, and harmonic current generated by a charging pile and peripheral power electronic equipment can be effectively filtered through a harmonic suppression function. However, the current multifunctional charging pile still has significant drawbacks in practical control. Firstly, the multifunctional control loop of reactive power regulation, VSG control, harmonic suppression and the like is usually designed independently, and the control parameters are preset according to single-function optimal or empirical values. The single-function parameter setting mode ignores the interaction influence among control functions, and when the voltage fluctuation of the power grid is severe, the harmonic background is complex or reactive power requirements are frequently changed, the controller with fixed parameters can possibly lead to the improvement of performance in one aspect at the cost of performance in other aspects, even the whole control fails, and the support potential of the charging pile as a distributed flexible resource on the power grid cannot be fully exerted. For example, an increased virtual inertia of the VSG to enhance the voltage supporting effect may slow down the response of the system to reactive demand changes, an increased controller gain to quickly suppress certain subharmonics may couple with the fundamental current control loop, even induce oscillations, impair the accuracy of reactive regulation, or otherwise, severe reactive power regulation may introduce additional voltage fluctuations and harmonic components. Therefore, a comprehensive control parameter optimization method is urgently needed, and the online or offline self-adaptive setting of the multifunctional control parameters of the charging pile can be realized from comprehensive consideration of a plurality of control targets such as reactive power, voltage and harmonic waves, so that the running performance and the grid connection friendliness of the charging pile are comprehensively improved in a complex power grid environment. Disclosure of Invention In order to overcome the defects, the invention provides a charging pile control parameter optimization method and device with an electric energy quality adjusting function. In a first aspect, a method for optimizing control parameters of a charging pile with a power quality adjustment function is provided, where the method for optimizing control parameters of a charging pile with a power quality adjustment function includes: Substituting the electric operation parameters of the charging pile power grid side into a pre-constructed charging pile control parameter optimization model and solving to obtain an optimization result corresponding to the charging pile control parameters; The optimized result is issued to a bottom layer controller of the charging pile, and the operation parameters of the charging pile are dynamically adjusted; The optimization result comprises at least one of virtual inertia of the virtual synchronous generator, virtual damping in a virtual synchronous generator algorithm, reactive power loop controller proportion parameters, reactive power loop controller integral parameters, voltage controller proportion parameters, voltage controller integral parameters, n-order harmonic controller proportion parameters and n-order harmonic controller integral parameters. Preferably, the pre-constructed charging pile control parameter optimization model comprises a multi-objective optimization function and a constraint condition corresponding to the multi-objective optimization function. Further, the multi-objective optimization function is as follows: In the above formula, F is a multi-objective optimization function value, Q ref is a reactive power reference value, Q is a real-t