A Power Quality Control Method, Device, Equipment and Medium for Energy Storage Inverters

Abstract

An embodiment of the present invention provides a method, device, equipment and medium for power quality control of an energy storage converter, including: obtaining a multi-objective optimization model of the energy storage converter constructed in advance and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity; for the compensation coefficient in the multi-objective optimization model, calculating the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient; controlling the energy storage converter based on the target parameter value of the compensation coefficient. Using this method, by constructing a multi-objective optimization model considering various influencing factors and compensation capacity, and based on the model evaluation results, harmonic and reactive current components generated by the load are offset in the energy storage converter, thereby realizing multi-objective optimization improvement of the power quality of the energy storage grid-connected system under different operating scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected power quality compensation control, and particularly to a power quality control method, device, equipment and medium for an energy storage converter. Background Art

[0002] With the accelerated construction of a new power system, a large number of high-proportion new elements such as distributed new energy represented by photovoltaic and wind power generation, new energy vehicles, new flexible loads, and new energy storage are connected to the distribution network on a large scale. The distribution network faces the challenge of the generalized carrying capacity of large-scale access of multiple multi-subject energies. On the one hand, the large-scale grid connection of flexible loads such as distributed energy and electric vehicles brings new challenges to the power quality and peak shaving and frequency modulation of the power grid. On the other hand, with the large-scale access of new flexible loads and distributed energy to the power grid, it will lead to a large-scale random change in the active and reactive power flow distribution of the power grid. The variability and randomness of the power grid power flow increase the difficulty of power grid voltage control, affect the power quality, and increase the system loss.

[0003] At present, grid-side energy storage and energy storage converters, as a new type of adjustable resource, can not only realize the efficient and flexible interaction and energy mutual assistance between energy storage batteries and the power grid, obtain price difference benefits, but also be used as an adjustable resource of the power grid to suppress the system power stability problems brought by the volatility and uncertainty of distributed resources, and realize the reactive power compensation of the power grid by the energy storage through changing the control mode of the energy storage converter, optimize the reactive power flow distribution, suppress power grid harmonics, and improve the power quality of the power grid.

[0004] However, the current grid-side energy storage fails to fully explore its reactive power support and power quality improvement potential, and the control mode of the energy storage converter is single, which cannot meet the support control requirements for stable and efficient operation of the system in different scenarios. In summary, there is an urgent need to propose an optimized operation control strategy for the energy storage converter to better utilize the remaining capacity of the energy storage inverter in different operation scenarios and optimize the power quality on the grid side. Summary of the Invention

[0005] The embodiments of the present invention provide a power quality control method, device, equipment and medium for an energy storage converter, realizing the multi-objective optimization and improvement of the power quality of the energy storage grid-connected system in different operation scenarios.

[0006] In a first aspect, the embodiments of the present invention provide a power quality control method for an energy storage converter, including:

[0007] Obtaining a multi-objective optimization model of the energy storage converter and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity;

[0008] For the compensation coefficient in the multi-objective optimization model, calculate the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient;

[0009] Control the energy storage converter based on the target parameter value of the compensation coefficient.

[0010] In a second aspect, an embodiment of the present invention provides an energy storage converter power quality control device, including:

[0011] A model acquisition module, configured to acquire a pre-constructed multi-objective optimization model of the energy storage converter and the corresponding constraint conditions of the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity;

[0012] A parameter determination module, configured to calculate the multi-objective optimization model for the compensation coefficient in the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient;

[0013] A control module, configured to control the energy storage converter based on the target parameter value of the compensation coefficient.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, including:

[0015] At least one processor; and a memory communicatively connected to the at least one processor;

[0016] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy storage converter power quality control method as described in the embodiment of the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the energy storage converter power quality control method as described in the embodiment of the first aspect when executed by a computer processor.

[0018] An embodiment of the present invention provides a method, device, equipment and medium for controlling the power quality of an energy storage converter. The method includes: first, obtaining a multi-objective optimization model of the energy storage converter constructed in advance and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity; then, for the compensation coefficient in the multi-objective optimization model, calculating the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient; finally, controlling the energy storage converter based on the target parameter value of the compensation coefficient. The above technical solution, different from the traditional energy storage grid-connected converter, adds the control idea of an active filter on the basis of the traditional energy storage converter. By constructing a multi-objective optimization model considering various influencing factors and compensation capacity at the grid connection point, and adding components used to offset the harmonic and reactive current generated by the load in the modulation wave of the energy storage converter based on the model evaluation results, the multi-objective optimization improvement of the power quality of the energy storage grid-connected system under different operating scenarios is realized.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of a method for controlling the power quality of an energy storage converter provided in Embodiment 1 of the present invention;

[0022] Figure 2 It is an example diagram of the topology structure of the energy storage converter constructed in Embodiment 1 of the present invention;

[0023] Figure 3 It is a block diagram of a reference current generation algorithm based on the method for controlling the power quality of an energy storage converter;

[0024] Figure 4 It is a schematic flowchart of another method for controlling the power quality of an energy storage converter provided in Embodiment 2 of the present invention;

[0025] Figure 5 It is a schematic diagram of the Parato boundary of the optimal target of the energy storage converter;

[0026] Figure 6It is a relationship diagram of the optimal compensation coefficient corresponding to the comprehensive power quality evaluation index and variables;

[0027] Figure 7 It is a schematic diagram of the waveform of the grid-connected current;

[0028] Figure 8 It is a schematic diagram of the waveforms of the grid-connected voltage and current;

[0029] Figure 9 It is a schematic diagram of the waveform of the grid-connected power;

[0030] Figure 10 It is a phase diagram of the grid-connected current;

[0031] Figure 11 It is a schematic structural diagram of a power quality control device for an energy storage converter provided in Embodiment III of the present invention;

[0032] Figure 12 It is a schematic structural diagram of an electronic device provided in Embodiment IV of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "original", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment I

[0036] Figure 1Schematic diagram of a method for controlling the power quality of an energy storage converter provided in Embodiment 1 of the present invention. This method is applicable to the situation of controlling the power quality of an energy storage converter based on multi-objective optimization compensation control. This method can be executed by an energy storage converter power quality control device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device.

[0037] As Figure 1 shown, the method for controlling the power quality of the energy storage converter provided in Embodiment 1 specifically may include the following steps:

[0038] S101. Obtain the pre-constructed multi-objective optimization model of the energy storage converter and the constraint conditions corresponding to the multi-objective optimization model.

[0039] Among them, the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity.

[0040] Traditional energy storage inverters only control active power. For example, if the battery has a certain capacity, they will try to generate as much power as possible. If connected to some remote areas or areas with relatively weak grid structures, fluctuations will occur on the grid side. The control method of traditional energy storage inverters does not consider the power quality problem on the grid side, so it cannot adapt to various scenarios, such as remote areas, areas with weak grid structures, or areas with a large amount of photovoltaic access. In this case, the power compensation method of traditional power energy storage converters is not very suitable. Therefore, in response to this problem, a method that takes into account both power quality and the remaining capacity of the energy storage is proposed in this embodiment. On the one hand, it can compensate for reactive power, and on the other hand, it can also transmit active power.

[0041] To more clearly describe the method for controlling the power quality of the energy storage converter provided in the embodiments of the present invention, Figure 2 Schematic diagram of the topology structure of the energy storage converter constructed in Embodiment 1 of the present invention. As Figure 2 shown, the DC side is an energy storage battery and a parallel bus capacitor C. The bus capacitor is a channel for energy exchange between the DC side and the AC side, and the DC side voltage can be stabilized by control. The structure of the energy storage converter selects a three-phase full-bridge inverter (i.e., the energy storage converter), which is composed of 6 insulated gate bipolar transistors (IGBTs). The filter selects an LCL-type filter, where L f is the filter inductor, R f is the parasitic resistance of the inductor, u oabc is the three-phase voltage output by the inverter, i oabc is the three-phase current output by the inverter after filtering. The grid load is connected in parallel at the point of common coupling (PCC) to simulate the influence of different types of loads on the power quality, uabc is the three-phase grid voltage. In the grid-connected operation mode, the inverter control strategy generally adopts constant power control (PQ control) to deliver stable power to the grid. Based on the idea of the active filter, the reference value to be controlled can be extracted, and then the reference value is input to the multi-functional controller. Based on this reference value, the active and reactive compensation coefficients are calculated and given to the inverter, so that the inverter outputs based on this reference value.

[0042] In this embodiment, both the optimization of the power quality on the grid side and the compensation capacity that the energy storage current limiter can provide are considered to obtain the active and reactive compensation coefficients, which is equivalent to giving the inverter a power reference value to the control circuit of the inverter. The control circuit will output the given target parameter value according to the underlying harmonic modulation or other modulation methods.

[0043] Considering that the power quality indexes of the energy storage grid-connected system generally include harmonic distortion, voltage fluctuation, voltage sag, voltage deviation, frequency deviation, three-phase unbalance, etc. A single evaluation of each index cannot reflect the power quality of the grid as a whole. Therefore, the comprehensive evaluation method of power quality in the traditional power system can be used for reference to organically combine multiple evaluation indexes into an overall index. By assigning different weights to each index, the single index accounts for different proportions in the overall index, and finally the comprehensive evaluation index is obtained. In this embodiment, the grid harmonic distortion rate and the power factor angle are used as the influencing factors of the comprehensive power quality evaluation index, and the two influencing factors are processed by maximum-minimum normalization to unify the dimension, and then the comprehensive evaluation model of the power quality of the energy storage grid-connected system is constructed.

[0044] In this embodiment, the comprehensive power quality evaluation model includes two sub-indexes: harmonic distortion rate and power factor. Considering that the influence degrees of these two sub-indexes on the comprehensive power quality evaluation index are different, when the energy storage converter manages the power quality, it is necessary to achieve the differentiated compensation of harmonics and reactive components. By introducing the active and reactive compensation coefficients into the energy storage converter control, the efficient utilization of the limited capacity is realized on the basis of optimizing the power quality of the energy storage converter.

[0045] The converter itself has a power transmission upper limit. For example, a 200-kilowatt converter can only have a rated power of 200 kilowatts or less. For example, if the reactive power compensation amount required on the grid side is larger than this, the remaining capacity is not enough. In this embodiment, an optimization is made on the premise of considering its own output power ability. The main thing is to support the power quality on the grid side and output as much active power as possible while ensuring that the power quality meets the requirements.

[0046] When the converter participates in power quality compensation, two objectives, namely the comprehensive power quality index and the input compensation capacity, are optimized, and a multi-objective optimization model of the energy storage converter is constructed based on these two objectives. After the multi-objective optimization model is pre-constructed, it is also necessary to set the conditions that different parameters in the objective optimization model should satisfy, which is denoted as the constraint conditions corresponding to the multi-objective optimization model in this embodiment. The reference current of the energy storage converter needs to include the fundamental wave current of grid-connected power generation and the compensation current for power quality control. This step is used to obtain the multi-objective optimization model of the energy storage converter and the constraint conditions corresponding to the multi-objective optimization model for subsequent model solving.

[0047] S102. For the compensation coefficient in the multi-objective optimization model, calculate the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient.

[0048] After establishing the multi-objective optimization model of the energy storage converter above, the multi-objective optimization model includes a harmonic compensation coefficient and a reactive power compensation coefficient, which can achieve the minimum input compensation capacity while meeting the comprehensive power quality evaluation index, so as to improve the utilization rate and efficiency of the energy storage device. In this embodiment, the multi-objective particle swarm algorithm is used to solve the multi-objective optimization model of the energy storage converter. Under the constraint conditions, the non-dominated solutions of the power quality index (i.e., the control effect) and the total compensation capacity (i.e., the control cost) are comprehensively considered. Then, based on the optimal compensation condition, the optimal solution is selected from the non-dominated solutions as the target parameter value of the compensation coefficient to determine the optimal active and reactive compensation coefficients of the reference current output by the converter, and better utilize the remaining capacity of the energy storage inverter to optimize the power quality on the grid side.

[0049] Preferably, the optimal compensation condition includes minimizing the input compensation capacity of the energy storage inverter while satisfying the optimal power quality. It can be understood that the optimal compensation strategy focuses on minimizing the input compensation capacity while satisfying the comprehensive power quality index. When the energy storage converter participates in power quality compensation, two objectives, namely the comprehensive power quality index and the input compensation capacity, need to be optimized. The optimal compensation strategy focuses on minimizing the input compensation capacity while satisfying the comprehensive power quality index. Power quality involves two indicators: harmonic distortion rate and power factor angle. It is hoped that the power factor angle of the power grid in the comprehensive power quality evaluation index is closer to the active power and the harmonic is lower. The power used by the inverter for reactive power compensation is minimized, and it is hoped that the power used for reactive power compensation is minimized.

[0050] S103. Control the energy storage converter based on the target parameter value of the compensation coefficient.

[0051] In this embodiment, the solution result obtained by solving the model based on the above steps is used as the target parameter value of the compensation coefficient. The solution result serves as the design basis for the reference current output of the energy storage converter, enabling full utilization of the capacity of the energy storage converter to meet the requirements of the comprehensive power quality index and realizing the precise governance function of the power grid power quality.

[0052] Exemplarily, Figure 3 is a block diagram of the reference current generation algorithm for the power quality control method based on the energy storage converter, as Figure 3 shown. This block diagram mainly includes three parts: instantaneous power calculation, compensation coefficient, and grid-connected active current calculation. Among them, u a , u b , u c are the three-phase grid-side voltages, i labc is the three-phase output of the energy storage converter, u d , u q are the output voltages in the d and q coordinate systems respectively, P and Q are the active and reactive powers at the grid connection point, i gd , i gq are the output currents in the d and q coordinate systems respectively, p, q, G, B, G1, B1 are intermediate state variables, LPF is the expression of the low-pass filter, i lpabc , i lqabc represent the filtered and modulated active and reactive current values respectively, and i refabc is the controller output current reference value. In this embodiment, based on grid connection, the energy storage converter adds the control idea of the active power filter. Through the harmonic detection link, in the current components output by the energy storage converter, harmonic and reactive current components used to cancel the harmonics generated by the load are added, so that the current flowing into the point of common coupling (PCC) presents a perfect sine wave. Thus, it can better utilize the remaining capacity of the energy storage inverter and optimize the power quality on the grid side.

[0053] An embodiment of the present invention provides a method for controlling the power quality of an energy storage converter. The method includes: first, obtaining a multi-objective optimization model of the energy storage converter constructed in advance and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality and the input compensation capacity of the energy storage grid-connected system; then, for the compensation coefficient in the multi-objective optimization model, calculating the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient; finally, controlling the energy storage converter based on the target parameter value of the compensation coefficient. The above technical solution, different from the traditional energy storage grid-connected converter, adds the control idea of an active filter on the basis of the traditional energy storage converter. By constructing a multi-objective optimization model considering various influencing factors and compensation capacity at the grid connection point, and adding components used to offset the harmonic and reactive current generated by the load in the modulation wave of the energy storage converter based on the model evaluation results, the multi-objective optimization improvement of the power quality of the energy storage grid-connected system under different operating scenarios is realized.

[0054] As an optional embodiment of the embodiment of the present invention, on the basis of the above embodiment, the construction steps of the multi-objective optimization model can be optimized, including:

[0055] a1) Determine the comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and power factor angle of the energy storage grid-connected system.

[0056] In this embodiment, the harmonic distortion rate and power factor angle of the energy storage grid-connected system are used as the comprehensive power quality evaluation indicators of the energy storage grid-connected system. The mutation series method is used to process the comprehensive power quality evaluation indicators to construct the comprehensive power quality evaluation model of the energy storage grid-connected system.

[0057] b1) Determine the compensation capacity model invested by the energy storage inverter according to the compensated harmonic power and reactive apparent power of the energy storage converter.

[0058] The compensation capacity invested by the energy storage converter is F2, which can be expressed as: In the formula, S1 represents the compensated harmonic power of the energy storage converter, and S2 represents the reactive apparent power of the energy storage converter.

[0059] As a specific implementation manner, the steps of constructing the comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and power factor angle of the energy storage grid-connected system can be optimized, including:

[0060] a11) Use the harmonic distortion rate and power factor angle of the energy storage grid-connected system as the comprehensive power quality evaluation indicators of the energy storage grid-connected system.

[0061] In this embodiment, the comprehensive power quality index of the energy storage inverter is composed of two sub-indicators: the harmonic distortion rate THD and the power factor PF.

[0062] Among them: THD = I h / I1, where: I h is the sum of the harmonic current components on the grid side; I1 is the fundamental wave component of any phase of the grid-connected current. Power factor angle PF: PF = cos -1 (P / (3VI1)), where V is the effective value of the grid voltage; P represents the active power value of the energy storage grid-connected system.

[0063] a12) Process the comprehensive power quality evaluation index according to the catastrophe progression method, and construct a comprehensive power quality evaluation model for the energy storage grid-connected system.

[0064] Among them, the catastrophe progression method is a comprehensive evaluation method for multi-level sorting analysis of the target to be evaluated. In the process of calculating the comprehensive index, it is not necessary to calculate the weight of a single index, which reduces the decisive role of human factors in calculating the weight value, and the calculation is simple and does not require a large amount of sample data. First, it is necessary to sort and layer the evaluation indexes to form an inverted tree-shaped comprehensive evaluation model, and then the corresponding catastrophe system type can be determined according to the obtained evaluation model.

[0065] In this embodiment, a comprehensive power quality evaluation index considering the grid harmonic distortion rate and the power factor angle is established. The grid harmonic distortion rate and the power factor angle are used as influencing factors of the comprehensive power quality evaluation index, and a power quality evaluation method based on the catastrophe progression method is established. The two influencing factors are processed by maximum-minimum standardization to unify the dimension, and a comprehensive power quality evaluation model for the energy storage grid-connected system is constructed.

[0066] Further, the processing of the comprehensive power quality evaluation index according to the catastrophe progression method to construct the comprehensive power quality evaluation model of the energy storage grid-connected system includes:

[0067] a121) Based on the comprehensive power quality evaluation index, combined with the mathematical model of the cusp catastrophe system, construct a catastrophe model of the comprehensive power quality evaluation index.

[0068] In this embodiment, the mathematical model of the cusp catastrophe system is adopted. According to the mathematical model of the cusp catastrophe system, the comprehensive power quality evaluation index can be obtained as a potential function, as shown in the following formula: f(y) = y 4 +x1y 2 +x2y, where y is the state variable; x1 represents the harmonic distortion rate of the grid-connected point current; x2 is the power factor angle of the grid-connected point. This potential function is used as the catastrophe model of the comprehensive power quality evaluation index.

[0069] a122) Standardize the comprehensive power quality evaluation index of the energy storage grid-connected system to obtain the comprehensive power quality evaluation index with unified dimension.

[0070] In this embodiment, based on the comprehensive evaluation index of power quality, considering the different dimensions of the harmonic distortion rate and power factor angle in the sub-indicators and the large difference in data, it is necessary to standardize the sub-indicators x1 and x2. Here, the maximum-minimum processing is adopted, and the standardized x1 and x2 can be expressed as x p1 and x p2 , as shown in the following formula:

[0071]

[0072] In the formula, x 1min and x 1max represent the minimum and maximum values of the harmonic distortion rate of the index respectively, and x 2min and x 2max represent the minimum and maximum values of the power factor angle of the index respectively.

[0073] a123) Process the mutation model of the comprehensive evaluation index of power quality to obtain the normalization formula.

[0074] In this embodiment, the equilibrium surface M of the cusp catastrophe system satisfies f′(y) = 0, and the singularity set S satisfies f″(y) = 0, and we get: By canceling the state variable y in the formula, the bifurcation set can be obtained: 8x1 3 +27x2 2 = 0. Expressing x1 and x2 in terms of the state variable y gives: x1 = -6y 2 , x2 = 8y 3 . Normalizing it can obtain [[ID=3 sixty]]

[0075] a124) Based on the normalization formula and the comprehensive evaluation index of power quality with unified dimensions, combined with the complementary decision-making principle in the cusp catastrophe system, construct a comprehensive evaluation model of the power quality of the energy storage grid-connected system.

[0076] According to the complementary decision-making principle in catastrophe theory, the comprehensive index F1 of the power quality evaluation model can be obtained, as shown in the following formula: Among them, x p1 represents the harmonic distortion rate of the energy storage grid-connected system after standardization, x p2 represents the power factor angle of the energy storage grid-connected system after standardization, S1 represents the compensated harmonic power of the energy storage converter, S2 represents the apparent power of the reactive power of the energy storage converter, and the meanings of other letters can be referred to the above explanations and will not be elaborated here.

[0077] c1) Based on the comprehensive evaluation model of power quality and the compensation capacity model, construct a multi-objective optimization model.

[0078] In this embodiment, a multi-objective optimization model is constructed based on the comprehensive power quality evaluation model and the compensation capacity model. Taking the optimal comprehensive power quality of the energy storage grid-connected system and the minimum input compensation capacity as the objective function, a multi-objective optimization model of the energy storage converter is established.

[0079] Preferably, the multi-objective optimization model is expressed as:

[0080] where F1 represents the comprehensive power quality evaluation index of the energy storage grid-connected system, F2 represents the compensation capacity input by the energy storage converter, x p1 represents the harmonic distortion rate of the energy storage grid-connected system after standardization, x p2 represents the power factor angle of the energy storage grid-connected system after standardization, S1 represents the compensation harmonic power of the energy storage converter, and S2 represents the reactive apparent power of the energy storage converter;

[0081] The constraint conditions are expressed as: and where α1 represents the harmonic compensation coefficient, α2 represents the reactive power compensation coefficient, x1 represents the harmonic distortion rate of the energy storage grid-connected system before standardization, x2 represents the power factor angle of the energy storage grid-connected system before standardization, x 10 represents the initial harmonic distortion rate of the energy storage grid-connected system before standardization, x 20 represents the initial power factor angle of the energy storage grid-connected system before standardization, x p10 represents the initial harmonic distortion rate of the energy storage grid-connected system after standardization, x p20 represents the initial power factor angle of the energy storage grid-connected system after standardization, U represents the effective value of the phase voltage on the grid side output by the energy storage converter, I1 represents the effective value of the output current, and Q0 represents the reactive power output by the energy storage inverter.

[0082] It should be noted that x 1min and x 1max represent the minimum and maximum values of the index harmonic distortion rate respectively, x 2min and x 2max represent the minimum and maximum values of the index power factor angle respectively. The compensated power quality index F1 should be between 0 and the initial power quality index, satisfying

[0083] The above technical solution specifies the construction steps of the multi-objective optimization model, providing a basis for obtaining the optimal active and reactive compensation coefficients of the energy storage converter by solving the model subsequently. Furthermore, based on the optimal compensation coefficients, the output reference current compensation control of the energy storage converter is carried out, which can better utilize the remaining capacity of the energy storage inverter and optimize the power quality on the grid side.

[0084] Embodiment 2

[0085] Figure 4 FIG. is a schematic flowchart of another energy storage converter power quality control method provided by Embodiment 2 of the present invention. This embodiment is a further optimization of the above embodiment. In this embodiment, further optimization is performed on the limitation of "calculating the multi-objective optimization model for the compensation coefficient in the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached to determine the target parameter value of the compensation coefficient".

[0086] As Figure 4 shown, Embodiment 2 of the present invention provides an energy storage converter power quality control method, which specifically includes the following steps:

[0087] S201. Obtain a pre-constructed multi-objective optimization model of the energy storage converter and the corresponding constraint conditions of the multi-objective optimization model.

[0088] Among them, the multi-objective optimization model is constructed based on the power quality of the energy storage grid-connected system and the input compensation capacity.

[0089] S202. For the compensation coefficient in the multi-objective optimization model, calculate the multi-objective optimization model based on the particle swarm optimization algorithm to obtain a set of candidate parameter values under the constraint conditions.

[0090] In this embodiment, a multi-objective particle swarm algorithm is used to solve the multi-objective optimization model, and a set of non-dominated solutions that comprehensively consider the power quality index, i.e., the governance effect, and the total compensation capacity, i.e., the governance cost, under the constraint conditions is obtained and denoted as the set of candidate parameter values.

[0091] S203. Based on the optimal compensation condition of the energy storage converter, determine the target parameter value of the compensation coefficient from the set of candidate parameter values.

[0092] In this embodiment, since the particles in the solution set are all feasible solutions for multi-objective optimization compensation, based on the principle of minimizing the input capacity on the premise of satisfying the power quality compensation effect, among several feasible solutions, the solution that satisfies the power quality index and has the relatively smallest capacity is selected as the final optimized compensation scheme to determine the optimal active and reactive compensation coefficients of the energy storage converter, that is, to determine the harmonic compensation coefficient α1 and the reactive power compensation coefficient α2.

[0093] S204. Control the energy storage converter based on the target parameter value of the compensation coefficient.

[0094] The above technical solution embodies the steps of determining the target parameter value of the compensation coefficient. In view of the requirements for the efficient and stable connection of grid-side energy storage to the power grid and its participation in the active power support and power quality improvement of the power grid under the background of a new power system, a power quality control method for a grid-connected energy storage converter based on multi-objective optimization compensation control is proposed. By constructing a comprehensive power quality index system for the energy storage grid-connected system considering the grid harmonic distortion rate and power factor angle, this method can accurately describe the power quality on the grid side of the system; and by comprehensively considering the comprehensive power quality index system and the compensation capacity input by the energy storage converter as the optimization control objectives, a multi-objective optimization model of the energy storage converter is established. By using the multi-objective particle swarm algorithm to solve the model, the optimal active and reactive compensation coefficients of the energy storage converter are obtained, and then a reference current compensation control method for the output of the energy storage converter is proposed. It can realize the multi-objective optimization improvement of the power quality of the grid-side energy storage under different scenarios and operating conditions in the background of the new power system. Compared with the traditional control method of the energy storage converter, it can make better use of the remaining capacity of the energy storage inverter and optimize the power quality on the grid side.

[0095] To verify the feasibility of the power quality governance of the energy storage converter, a simulation was built in the simulation platform Matlab. Table 1 shows the specific parameters used for the power quality verification of the energy storage converter. As shown in Table 1, the inverter power range is 10 kW, and the DC bus voltage U dc is 750 V. The content in the table will not be listed one by one here.

[0096] Table 1

[0097] Inverter power kW 10 <![CDATA[DC bus voltage U dc / V]]> 750 Grid-side voltage U / V 380 <![CDATA[Filter inductor L f / mH]]> 2 Filter capacitor C / μF 44 <![CDATA[Uncontrolled rectifier bridge load resistor R h / Ω]]> 20 <![CDATA[Inductive load (resistance R L / Ω, inductance L L / mH)]]> 10，1

[0098] Before performing multi-objective optimization compensation, it is necessary to first obtain the initial harmonic distortion rate THD0 of the grid-connected current and the initial power factor angle PF0 of the current to calculate the initial comprehensive power quality evaluation index. It can be obtained that THD0 = 0.213, PF0 = 0.94. After calculation, the initial comprehensive power quality evaluation index F1 = 0.73. According to the previous constraint conditions, the Pareto boundary (i.e., the Pareto optimal solution) of the multi-objective optimization compensation model of the energy storage converter is calculated.

[0099] Figure 5 It is a schematic diagram of the Parato boundary of the optimal target of the energy storage converter, as Figure 5As shown, it can be seen that the abscissa is the comprehensive power quality evaluation index F1, and the ordinate is the input compensation capacity F2. When F2 = 0, that is, when the compensation capacity input by the energy storage converter is 0, the value of the comprehensive power quality evaluation index is the largest, and the initial power quality index value is 0.73. When the input capacity F2 increases, the corresponding comprehensive index F1 decreases accordingly. When the input capacity is 4.67 kVA, that is, when the energy storage converter performs full compensation, the power quality index drops to 0, and there is no power quality problem at the grid connection point. However, considering that the remaining apparent capacity of the energy storage converter for improving power quality is limited and may not be sufficient to compensate for all harmonic and reactive currents, it is necessary to optimize the design of compensation parameters and improve the power quality of the PCC current according to the relationship between the compensation capacity F2 and the comprehensive index F1 in the Parato boundary.

[0100] Figure 6 It is a relationship diagram of the optimal compensation coefficient corresponding to the comprehensive power quality evaluation index and the variable. As Figure 6 shown, the abscissa is the comprehensive power quality evaluation index F1, and the ordinates are the harmonic compensation coefficient α1, the reactive power compensation coefficient α2, the harmonic distortion rate x1 of the grid connection point current, and the power factor angle x2 of the grid connection point. After selecting a desired comprehensive power quality evaluation index F1, according to Figure 6 the compensation coefficient that meets the Parato optimum can be obtained. The desired comprehensive power quality index F1 of the simulation design is effectively reduced, that is, F1 = 0.18. From Figure 6 the harmonic and reactive compensation coefficients α1 = 0.71 and α2 = 0.53 are obtained.

[0101] In order to more intuitively observe the improvement effect of the grid connection point current under different degrees of power quality compensation, compensation schemes for different time periods are set. The simulation settings are as follows: no compensation is performed from 0 to 0.04 s, only harmonic components are compensated during 0.04 to 0.08 s, full compensation for harmonic and reactive components is performed during 0.08 to 0.12 s, and the control strategy is changed to optimized compensation during 0.12 to 0.16 s.

[0102] Figure 7 It is a schematic diagram of the grid connection point current waveform. Figure 8 It is a schematic diagram of the grid connection point voltage and current waveforms. Figure 9 It is a schematic diagram of the grid connection point power waveform. Figure 7 In it, the abscissa is time and the ordinate is current. Figure 8 In it, the abscissa is time and the ordinate is voltage. Figure 9 In it, the abscissa is time and the ordinate is power. P represents active power and Q represents reactive power. As Figure 7As shown, during the initial stage from 0 to 0.04 s, the energy storage converter is not put into compensation. The grid-connected current contains active, reactive, and harmonic components, and the waveform distortion rate is relatively large. This is reflected in the power as fluctuations in the grid-side active and reactive powers. As Figure 8 shown, after 0.04 s, the energy storage converter first compensates for the harmonic components. The harmonic components are completely compensated, and the waveform of the grid-connected current becomes a sine wave. The grid-connected power is balanced without fluctuations. Due to the existence of the reactive component, the grid-connected reactive power is not zero. Observe Figure 8 and it can be seen that there is still a phase difference between the grid-connected voltage and current. From 0.08 to 0.12 s, after compensating for the harmonic components, the reactive components are completely compensated. Observe Figure 7 and it can be found that since the reactive components of the grid-connected current are completely compensated, the current amplitude will slightly decrease, and the current and voltage at the grid connection point are in the same phase. At this time, the reactive power is provided by the energy storage converter, Figure 9 and the grid-connected reactive power in Figure 10 basically drops to 0 Var. After 0.12 s, the control strategy is changed to optimized compensation. Since the capacity required for full compensation is relatively large, the compensation capacity is input according to the actual given power quality index. In the optimized compensation strategy, the harmonic and reactive components do not need to be fully compensated. Figure 10 is the phase diagram of the grid-connected current. Through the phase diagram, the specific conditions of the current in each stage before and after compensation can be seen more intuitively. As

[0103] shown, the abscissa and ordinate are the currents of different phases. Compared with the phase diagram of the grid-connected current without compensation, the phase diagram of the grid-connected current corresponding to the optimized compensation is closer to the phase diagram of the grid-connected current corresponding to the full compensation.

[0104] Embodiment 3

[0105] Figure 11 is a schematic structural diagram of an energy storage converter power quality control device provided by Embodiment 3 of the present invention. This device is applicable to the situation of controlling the power quality of an energy storage converter based on multi-objective optimized compensation control. This energy storage converter power quality control device can be configured in an electronic device. As Figure 11 shown, this device includes: a model acquisition module 31, a parameter determination module 32, and a control module 33; among them,

[0106] The model acquisition module 31 is configured to acquire a multi-objective optimization model of a energy storage converter and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality and the input compensation capacity of the energy storage grid-connected system;

[0107] The parameter determination module 32 is configured to calculate the multi-objective optimization model for the compensation coefficient until the optimal compensation condition of the energy storage converter is reached, so as to determine the target parameter value of the compensation coefficient;

[0108] The control module 33 is configured to control the energy storage converter based on the target parameter value of the compensation coefficient.

[0109] The above technical solution, different from the traditional energy storage grid-connected converter, adds the control idea of the active filter on the basis of the traditional energy storage converter. By constructing a multi-objective optimization model considering various influencing factors and compensation capacity at the grid connection point, and adding components in the modulation wave of the energy storage converter to offset the harmonic and reactive current components generated by the load based on the model evaluation results, so as to realize the multi-objective optimization improvement of the power quality of the energy storage grid-connected system under different operating scenarios.

[0110] Optionally, the device further includes a model construction module, including:

[0111] The first construction unit is configured to determine a comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and the power factor angle of the energy storage grid-connected system;

[0112] The second construction unit is configured to determine a compensation capacity model for the input of the energy storage inverter according to the compensated harmonic power and the reactive apparent power of the energy storage converter;

[0113] The model construction unit is configured to construct a multi-objective optimization model based on the comprehensive power quality evaluation model and the compensation capacity model.

[0114] Optionally, the first construction unit is specifically configured to:

[0115] Take the harmonic distortion rate and the power factor angle of the energy storage grid-connected system as the comprehensive power quality evaluation indexes of the energy storage grid-connected system;

[0116] Process the comprehensive power quality evaluation indexes according to the mutation series method to construct a comprehensive power quality evaluation model of the energy storage grid-connected system.

[0117] Optionally, processing the comprehensive power quality evaluation indexes according to the mutation series method to construct a comprehensive power quality evaluation model of the energy storage grid-connected system includes:

[0118] Based on the comprehensive power quality evaluation indexes, combined with the mathematical model of the cusp catastrophe system, construct a mutation model of the comprehensive power quality evaluation indexes;

[0119] Standardize the comprehensive power quality evaluation index of the energy storage grid-connected system to obtain the comprehensive power quality evaluation index with a unified dimension;

[0120] Process the mutation model of the comprehensive power quality evaluation index to obtain a normalization formula;

[0121] Based on the normalization formula and the comprehensive power quality evaluation index with a unified dimension, and combining the complementary decision-making principle in the cusp catastrophe system, construct a comprehensive power quality evaluation model for the energy storage grid-connected system.

[0122] Optionally, the multi-objective optimization model is expressed as:

[0123] where F1 represents the comprehensive power quality evaluation index of the energy storage grid-connected system, F2 represents the compensation capacity invested by the energy storage converter, x p1 represents the harmonic distortion rate of the energy storage grid-connected system after standardization, x p2 represents the power factor angle of the energy storage grid-connected system after standardization, S1 represents the compensated harmonic power of the energy storage converter, and S2 represents the reactive apparent power of the energy storage converter;

[0124] The constraint conditions are expressed as: and where α1 represents the harmonic compensation coefficient, α2 represents the reactive power compensation coefficient, x1 represents the harmonic distortion rate of the energy storage grid-connected system before standardization, x2 represents the power factor angle of the energy storage grid-connected system before standardization, x 10 represents the initial harmonic distortion rate of the energy storage grid-connected system before standardization, x 20 represents the initial power factor angle of the energy storage grid-connected system before standardization, x p10 represents the initial harmonic distortion rate of the energy storage grid-connected system after standardization, x p20 represents the initial power factor angle of the energy storage grid-connected system after standardization, U represents the effective value of the phase voltage on the grid side output by the energy storage converter, I1 represents the effective value of the output current, and Q0 represents the reactive power output by the energy storage inverter.

[0125] Optionally, the parameter determination module 32 is specifically used for:

[0126] For the compensation coefficients in the multi-objective optimization model, calculate the multi-objective optimization model based on the particle swarm optimization algorithm to obtain a set of candidate parameter values under the constraint conditions;

[0127] Determine the target parameter values of the compensation coefficients from the set of candidate parameter values based on the optimal compensation conditions of the energy storage converter.

[0128] Optionally, the optimal compensation condition includes minimizing the compensation capacity invested by the energy storage inverter while satisfying the optimal power quality.

[0129] The energy storage converter power quality control device provided by the embodiments of the present invention can execute the energy storage converter power quality control method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0130] Embodiment 4

[0131] Figure 12 FIG. 10 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0132] As Figure 12 shown, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. The memory stores a computer program executable by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0133] A plurality of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the energy storage converter power quality control method.

[0135] In some embodiments, the energy storage converter power quality control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the energy storage converter power quality control method described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the energy storage converter power quality control method by any other suitable means (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0139] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0141] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0142] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0143] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A power quality control method for an energy storage converter, characterized in that Including: Obtain a multi-objective optimization model of a pre-built energy storage converter and the constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality and input compensation capacity of an energy storage grid-connected system; For the compensation coefficient in the multi-objective optimization model, calculate the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached, so as to determine the target parameter value of the compensation coefficient; Control the energy storage converter based on the target parameter value of the compensation coefficient; Among them, the construction steps of the multi-objective optimization model include: Determine the comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and power factor angle of the energy storage grid-connected system; Determine the compensation capacity model of the energy storage inverter input according to the compensated harmonic power and reactive apparent power of the energy storage converter; Construct the multi-objective optimization model based on the comprehensive power quality evaluation model and the compensation capacity model; Among them, constructing the comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and power factor angle of the energy storage grid-connected system includes: Take the harmonic distortion rate and power factor angle of the energy storage grid-connected system as the comprehensive power quality evaluation index of the energy storage grid-connected system; Process the comprehensive power quality evaluation index according to the mutation series method to construct the comprehensive power quality evaluation model of the energy storage grid-connected system; Among them, processing the comprehensive power quality evaluation index according to the mutation series method to construct the comprehensive power quality evaluation model of the energy storage grid-connected system includes: Based on the comprehensive power quality evaluation index, combine the mathematical model of the cusp catastrophe system to construct a mutation model of the comprehensive power quality evaluation index; Perform standardization processing on the comprehensive power quality evaluation index of the energy storage grid-connected system to obtain the comprehensive power quality evaluation index after unified dimension; Process the mutation model of the comprehensive power quality evaluation index to obtain a normalization formula; Based on the normalization formula and the comprehensive power quality evaluation index after unified dimension, combine the complementary decision-making principle in the cusp catastrophe system to construct the comprehensive power quality evaluation model of the energy storage grid-connected system.

2. The method according to claim 1, wherein The multi-objective optimization model is expressed as: Among them, F1 represents the comprehensive power quality evaluation index of the energy storage grid-connected system, F2 represents the compensation capacity invested by the energy storage converter, x p1 represents the harmonic distortion rate of the standardized energy storage grid-connected system, x p2 represents the power factor angle of the standardized energy storage grid-connected system, S1 represents the compensated harmonic power of the energy storage converter, and S2 represents the reactive apparent power of the energy storage converter; The constraint condition is expressed as: and where α1 represents the harmonic compensation coefficient, α2 represents the reactive power compensation coefficient, x1 represents the harmonic distortion rate of the energy storage grid-connected system before normalization, x2 represents the power factor angle of the energy storage grid-connected system before normalization, x 10 represents the initial harmonic distortion rate of the energy storage grid-connected system before normalization, x 20 represents the initial power factor angle of the energy storage grid-connected system before normalization, U represents the effective value of the phase voltage on the grid side output by the energy storage converter, x p10 represents the initial harmonic distortion rate of the energy storage grid-connected system after normalization, x p20 represents the initial power factor angle of the energy storage grid-connected system after normalization, I1 represents the effective value of the output current, and Q0 represents the reactive power output by the energy storage inverter.

3. The method according to claim 1, wherein For the compensation coefficient in the multi-objective optimization model, calculating the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached, so as to determine the target parameter value of the compensation coefficient includes: For the compensation coefficient in the multi-objective optimization model, calculate the multi-objective optimization model based on the particle swarm optimization algorithm to obtain a set of candidate parameter values under the constraint conditions; Based on the optimal compensation condition of the energy storage converter, determine the target parameter value of the compensation coefficient from the set of candidate parameter values.

4. The method according to claim 1, wherein The optimal compensation condition includes minimizing the compensation capacity input by the energy storage inverter while satisfying the optimal power quality.

5. A power quality control device for an energy storage converter, characterized in that, Including: A model acquisition module, configured to acquire a multi-objective optimization model of a energy storage converter and constraint conditions corresponding to the multi-objective optimization model, where the multi-objective optimization model is constructed based on the power quality and input compensation capacity of an energy storage grid-connected system; A parameter determination module, configured to calculate the multi-objective optimization model for the compensation coefficient in the multi-objective optimization model until the optimal compensation condition of the energy storage converter is reached, so as to determine the target parameter value of the compensation coefficient; A control module, configured to control the energy storage converter based on the target parameter value of the compensation coefficient; The device further includes a model construction module, including: A first construction unit, configured to determine a comprehensive power quality evaluation model of the energy storage grid-connected system according to the harmonic distortion rate and power factor angle of the energy storage grid-connected system; A second construction unit, configured to determine a compensation capacity model for the input of the energy storage inverter according to the compensated harmonic power and reactive apparent power of the energy storage converter; A model construction unit, configured to construct the multi-objective optimization model based on the comprehensive power quality evaluation model and the compensation capacity model; Wherein, the first construction unit is specifically configured to: Use the harmonic distortion rate and power factor angle of the energy storage grid-connected system as comprehensive power quality evaluation indicators of the energy storage grid-connected system; Process the comprehensive power quality evaluation indicators according to the mutation series method to construct a comprehensive power quality evaluation model of the energy storage grid-connected system; Wherein, the process of processing the comprehensive power quality evaluation indicators according to the mutation series method to construct a comprehensive power quality evaluation model of the energy storage grid-connected system includes: Based on the comprehensive power quality evaluation indicators, combine with the mathematical model of the cusp catastrophe system to construct a mutation model of the comprehensive power quality evaluation indicators; Perform standardization processing on the comprehensive power quality evaluation indicators of the energy storage grid-connected system to obtain the comprehensive power quality evaluation indicators after unified dimension; Process the mutation model of the comprehensive power quality evaluation indicators to obtain a normalization formula; Based on the normalization formula and the comprehensive power quality evaluation indicators after unified dimension, combine with the complementary decision-making principle in the cusp catastrophe system to construct a comprehensive power quality evaluation model of the energy storage grid-connected system.

6. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the energy storage converter power quality control method according to any one of claims 1-4.

7. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the energy storage converter power quality control method according to any one of claims 1-4 when executed by a computer processor.

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