Energy storage converter dynamic voltage regulation control method and device based on extremum search, and medium

Abstract

The invention discloses an extreme value search-based dynamic voltage regulation control method and device for an energy storage converter, and a medium, and the method comprises the steps: constructing a partition voltage model, carrying out the regional division of a power distribution network scene, and calculating a voltage risk index corresponding to each region; when the voltage risk index corresponding to a certain area is greater than a threshold value, performing voltage regulation on the area; comprising the following steps: constructing an energy storage converter control model which comprises an outer-loop power control loop and an inner-loop current control loop which cooperate with each other; wherein the outer loop power control loop is used for outputting reference voltage or reactive power according to target voltage; the inner loop current control loop responds to the reference voltage output by the outer loop power control loop and controls the voltage by adjusting the current output by the converter; setting a voltage control objective function, and solving the voltage control objective function based on an extremum search algorithm according to the energy storage converter control model to obtain optimal reference active power and reactive power; and obtaining a current inner ring reference value according to the optimal reference active power and reactive power.

Description

Technical Field

[0001] The present invention belongs to the field of voltage optimization control in the scenario of distribution network zoning, and particularly relates to a dynamic voltage regulation control method, device, and medium for an energy storage converter based on extremum search. Background Art

[0002] With the large-scale access of distributed energy, its output is significantly random and volatile due to natural factors such as sunlight and wind. In addition, due to the dynamic changes in load demand at different times, the operating conditions of the distribution network become increasingly complex, and voltage stability faces severe challenges. Most traditional distribution network voltage regulation strategies are based on fixed threshold control principles, that is, within the pre-set upper and lower voltage threshold ranges, conventional means such as adjusting the tap of the on-load tap-changer transformer and switching reactive power compensation devices are used to maintain voltage stability. Although these methods can play an important role in a relatively simple power network architecture, in the context of the current high penetration rate of distributed energy, these methods have many limitations. For example, fixed thresholds are difficult to track the rapid changes in the system operating state in real time, and problems such as adjustment lag, overshoot, or undershoot are likely to occur, and it is impossible to accurately match complex and variable voltage requirements, thus making it difficult to ensure the reliable operation of the distribution network. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a dynamic voltage regulation control method, device, and medium for an energy storage converter based on extremum search.

[0004] In a first aspect, an embodiment of the present invention provides a dynamic voltage regulation control method for an energy storage converter based on extremum search, and the method includes:

[0005] Construct a zonal voltage model to divide the distribution network scenario into regions, and calculate the voltage risk index corresponding to each region;

[0006] When the voltage risk index corresponding to a certain region is greater than the threshold, voltage regulation is performed on this region; wherein, the process of performing voltage regulation on this region includes:

[0007] Construct an energy storage converter control model, and the energy storage converter control model includes an outer-loop power control loop and an inner-loop current control loop that cooperate with each other; wherein, the outer-loop power control loop is used to output a reference voltage or reactive power according to the target voltage; the inner-loop current control loop, in response to the reference voltage output by the outer-loop power control loop, controls the voltage by adjusting the current output by the converter.

[0008] Set a voltage control objective function, and based on the extremum search algorithm, solve the voltage control objective function according to the energy storage converter control model to obtain the optimal reference active power and the optimal reference reactive power;

[0009] Obtain the reference value of the current inner loop according to the optimal reference active power and the optimal reference reactive power.

[0010] In a second aspect, an embodiment of the present invention provides an electronic device, including:

[0011] At least one processor; and

[0012] A memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the above-mentioned energy storage converter dynamic voltage regulation control method based on extremum search.

[0014] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program realizes the above-mentioned energy storage converter dynamic voltage regulation control method based on extremum search when executed by a processor.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instructions, and the computer program / instructions realize the above-mentioned energy storage converter dynamic voltage regulation control method based on extremum search when executed by a processor.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] The present invention provides an energy storage converter dynamic voltage regulation control method based on extremum search. The extremum search algorithm is based on system output feedback, introduces an online disturbance signal, continuously monitors the change trend of the system performance index after the disturbance, and uses an adaptive mechanism to dynamically adjust the control parameters, driving it to approach and lock the optimal reference active power and the optimal reference reactive power at a faster convergence speed. In the application scenario of distribution network voltage regulation, the extremum search algorithm can get rid of the dependence on an accurate system model, and with its excellent real-time optimization ability, it can flexibly regulate directly according to the real-time feedback information of voltage fluctuations, and can effectively cope with the uncertainty problems brought by the access of distributed energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1Flow chart of the dynamic voltage regulation control method for energy storage converters based on extremum search provided by the embodiments of the present invention;

[0020] Figure 2 Schematic diagram of an electronic device provided by the embodiments of the present invention. Specific embodiments

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] It should be noted that, without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0023] Step S1: Construct a partition voltage model to divide the distribution network scenario into regions, and calculate the voltage risk index corresponding to each region.

[0024] Furthermore, in the dynamic voltage regulation scenario of the distribution network, establishing an accurate partition voltage model and risk quantification system is the prerequisite for implementing the control strategy. After dividing the physical boundary through comprehensive analysis of regional electrical characteristics, geographical distribution, and load characteristics, it is necessary to construct a real-time voltage monitoring network and design a risk warning mechanism. In this example, node data is obtained through high-frequency sampling, and combined with the sliding window filtering and dynamic threshold adjustment algorithms, abnormal fluctuations are effectively eliminated to ensure that the voltage signal input to the control system has high credibility. Furthermore, by normalizing the risk index, multi-dimensional electrical parameters are mapped into a unified quantization value, providing a scientific decision-making basis for the priority response of subsequent energy storage converters.

[0025] Now assume that in a specific distribution network scenario, based on the above comprehensive consideration factors, the entire distribution network is divided into N regions, and each region contains M i nodes. The process of constructing the partition voltage model is as follows:

[0026] First, the instantaneous node voltage value V i,k (t) is obtained in real time through an intelligent electricity meter or a phasor measurement unit (PMU), where the sampling frequency is 100 Hz.

[0027] Then, the sliding window method is used to detect abnormal data. If the voltage of a certain node deviates from the regional average by more than a certain threshold, it is marked as abnormal and excluded. The average value of the effective node voltage after exclusion is:

[0028]

[0029] In the formula, M i ' is the number of effective nodes after removing anomalies, which can avoid interference from noise data. Ω is the effective node, k is the k-th effective node, and i is the i-th area;

[0030] Finally, to prevent the influence of instantaneous fluctuations, the exponential weighted moving average is used to calculate the voltage mean value of the i-th area The expression is as follows:

[0031]

[0032] In the formula, α is the smoothing coefficient, and a reference value can be 0.2. The time interval Δt is 10 ms. This formula can ensure that the newer the data, the higher the weight, and it can reflect the real-time change trend of the voltage.

[0033] Furthermore, the voltage risk index can be measured by the basic risk index and the dynamic risk threshold. Among them, the basic risk index uses the root mean square error of voltage to measure the overall deviation degree of the voltage of each node in the area from the rated value:

[0034]

[0035] In the formula, is the root mean square error. The larger this value is, the more unstable the voltage is. V ref is the rated value of the voltage.

[0036] The dynamic risk threshold R th needs to be dynamically adjusted with the power fluctuation of the distributed power source. The expression is as follows:

[0037] R th = R base + β·ΔP dg (4)

[0038] ΔP dg = |P dg (t) - P dg (t - T)| (5)

[0039] In the formula, R base is the basic threshold, corresponding to the scenario without disturbance from the distributed power source. A reference value can be 0.05 p.u.. P dg is the output power of the distributed power source, and ΔP dg represents the power change amount of the distributed power source within the time window T = 1 min. The unit of this value is kW. β is the sensitivity coefficient, reflecting the influence of power fluctuation on the threshold. A reference value can be 0.001 p.u. / kW.

[0040] To facilitate cross - regional comparison, the voltage risk index is mapped to the interval [0, 1]:

[0041]

[0042] If indicates that the voltage risk is controllable and no voltage regulation is required; if indicates that the risk exceeds the limit and a voltage regulation action needs to be triggered.

[0043] Step S2, when the voltage risk index corresponding to a certain region is greater than the threshold, voltage regulation is performed on this region; among them, the process of performing voltage regulation on this region includes:

[0044] Step S201, construct a control model for the energy storage converter, and the control model for the energy storage converter includes an outer - loop power control loop and an inner - loop current control loop that cooperate with each other; among them, the outer - loop power control loop is used to output a reference voltage or reactive power according to the target voltage; the inner - loop current control loop, in response to the reference voltage output by the outer - loop power control loop, controls the voltage by adjusting the current output by the converter.

[0045] It should be noted that the energy storage converter is the core electrical equipment for realizing the bidirectional energy flow between the energy storage system and the power grid, and its control model for the energy storage converter plays a decisive role in ensuring the efficient, stable and safe operation of the energy storage system. The control model of the energy storage converter can adopt a composite architecture of "double - loop control + risk compensation" (see Figure 1 ). Among them, the double - loop control is composed of an outer - loop power control loop and an inner - loop current control loop that cooperate with each other. The outer - loop voltage controller is mainly responsible for setting the overall voltage target. It calculates the reference voltage or reactive power that needs to be adjusted according to the real - time voltage situation of the power grid, combined with the preset planned value and risk assessment parameters. This reference value is transmitted to the inner - loop current controller as the control target of the inner - loop. For example, when the voltage of a certain region is low, the outer - loop controller will calculate the reactive power that needs to be increased to raise the voltage, and then send this instruction to the inner - loop. The inner - loop current controller is responsible for quickly responding to the instruction of the outer - loop and precisely controlling the voltage by adjusting the current output by the converter. The inner - loop needs to handle faster dynamic changes, such as the instantaneous voltage fluctuations of the power grid, by adjusting the d - axis and q - axis components of the current to ensure that the actual output current quickly tracks the target value set by the outer - loop. For example, when the outer - loop requires an increase in reactive power, the inner - loop will adjust the reactive component (q - axis) of the current to make the converter output more reactive current, thereby raising the voltage.

[0046] Furthermore, the control parameters of the outer - loop power control loop include an active - power regulation channel and a reactive - power regulation channel; among them, the expression of the active - power regulation channel is as follows:

[0047]

[0048] Wherein, P ref is the reference active power, and P sch is the planned active power, K p is the active power ratio coefficient, H zone is the inertia time constant of the partition, indicating the inertia support ability of the energy storage and generators in this area for frequency. T droop is the droop time constant, which determines the power adjustment speed. The typical value is 2 - 5 seconds. If it is too small, it will cause oscillation, and if it is too large, the response will be slow. ω grid is the actual angular frequency of the power grid, and ω nom is the nominal angular frequency, is the voltage risk index (calculated according to formula 6). ΔP vsg is the compensation power of the virtual synchronous machine. The virtual inertia J vir simulates the rotor inertia of the synchronous machine and suppresses the frequency change rate The virtual damping D vir provides a damping torque to reduce the amplitude of frequency oscillation; Δω represents the difference between the actual angular frequency and the nominal angular frequency.

[0049] Furthermore, the expression of the reactive power regulation channel is as follows:

[0050]

[0051] Wherein, Q ref is the reference reactive power, and Q sch is the planned reactive power, K q is the reactive power gain coefficient; X filter is the filter reactance, which is used to suppress the converter switching harmonics; represents the square value of the nominal voltage; S rated is the rated capacity of the converter, is the voltage mean value of the i-th area, V nom is the nominal voltage, V i max , V i min are respectively the upper and lower voltage limits allowed for the safe operation of the i-th area, and γ υ is the voltage risk gain.

[0052] Furthermore, the control parameters of the inner loop current control loop include the d-axis current regulation channel and the q-axis current regulation channel, which usually cover the proportional-integral (PI) gain, decoupling compensation term, and feed-forward voltage component to achieve fast tracking and decoupling control of the active and reactive components.

[0053] In a power electronic converter, the coupling effect of the direct and quadrature axis currents is eliminated through the decoupling matrix in the dq coordinate system, so that the active and reactive components can be independently controlled. The decoupling control equation decomposes a multivariable coupling system into multiple independent single-input single-output subsystems through mathematical transformation, as follows:

[0054]

[0055] In the formula, i d ,i q are the d-axis / q-axis reference voltages, reference currents, and actual currents respectively. K p is the proportionality coefficient, K i is the integral coefficient, s is the Laplace operator, ω is the grid angular frequency, L is the filter inductor, and V PCC is the amplitude of the grid-connected point voltage.

[0056] Step S202: Set the voltage control objective function. According to the energy storage converter control model, solve the voltage control objective function based on the extremum seeking algorithm to obtain the optimal reference active power and the optimal reference reactive power.

[0057] It should be noted that although the traditional double-loop control architecture can achieve basic power regulation, there are problems such as response lag and steady-state error when dealing with random disturbances of distributed energy. Therefore, in this example, an extremum seeking algorithm (ES) is introduced on top of the double-loop control layer as a global optimizer to form a closed-loop control link of "dynamic perception - parameter optimization - precise execution".

[0058] Extremum Seeking (ES) is a model-free optimization method that estimates the gradient of the objective function by injecting high-frequency perturbations, thereby dynamically adjusting the control parameters to find the extremum point. This is used in the distribution network to adjust the active and reactive power outputs of the energy storage converter in real time to cope with voltage fluctuations. The double-loop control of the energy storage converter usually includes an outer loop (power loop) and an inner loop (current loop). The outer loop is responsible for generating active / reactive power commands based on voltage / power deviations, while the inner loop quickly tracks the commands through high-bandwidth control to ensure dynamic response. The combination of the dynamic optimization ability of ES and the fast response of the double-loop control can improve the overall stability and efficiency of the system.

[0059] First, inject high-frequency sinusoidal perturbations into the current control parameters, that is, the reference active power P ref and the reference reactive power Q ref . The expression is as follows:

[0060]

[0061] μ0 = [P ref ,Qref (14)

[0062] where a is the disturbance amplitude, usually taking 5% - 10% of the control variable range, ω p , ω q is the disturbance frequency, with a value range of 10 - 100 Hz, and satisfies ω p ≠ω q to decouple the active / reactive power channels.

[0063] Secondly, calculate the voltage control objective function in real time:

[0064]

[0065] where V i,k is the node voltage data collected in real time through PMU or smart meters, V ref is the rated value of the voltage, S rated is the rated capacity of the converter, and λ indicates that the higher the regional voltage risk, the more priority is given to voltage regulation.

[0066] Then extract the gradient component through a low-pass filter, and the transfer function of the filter is:

[0067]

[0068] Filter the output after the target function J(u) is aliased with the disturbance signal:

[0069]

[0070] where LPF[x(t)] represents low-pass filtering the signal x(t).

[0071] Finally, update the control parameters along the negative gradient direction to minimize the target function J(u):

[0072]

[0073] γ = 0.5·a 2 (21)

[0074] When the maximum number of iterations 100 is satisfied or the relative change amount is less than a certain threshold, that is , stop the above optimization process.

[0075] Step S203, obtain the current inner-loop reference value according to the optimal reference active power and the optimal reference reactive power.

[0076] The power command optimized by the extremum search algorithm is converted into the current inner-loop reference value through the dq decoupling equation, completing the instruction transfer from the strategy layer to the execution layer. The finally generated The instruction is pulse-width modulated to drive the power devices of the converter, enabling rapid injection / absorption of reactive current and bidirectional regulation of active power, forming a complete technical closed-loop from voltage sensing, risk assessment, parameter optimization to power execution.

[0077] The output instruction of the energy storage current device is calculated by the following formula:

[0078]

[0079] In the formula, is the reference value of the current inner loop, which can achieve rapid tracking of voltage changes; represents the optimal reference active power obtained by solving with the ES algorithm, represents the optimal reference reactive power obtained by solving with the extreme value search algorithm, V PCC represents the amplitude of the grid-connected voltage, and j represents the imaginary number.

[0080] In summary, the present invention provides a dynamic voltage regulation control method for an energy storage converter based on extreme value search. By integrating the partition voltage model, dynamic risk index and extreme value search optimization algorithm, it can significantly improve the dynamic response speed and stability of the distribution network voltage control. Through the collaborative mechanism of double-loop control and high-frequency disturbance optimization, the system gets rid of the dependence on the accurate model and can still achieve adaptive regulation in complex working conditions. It can be adapted to the scenario of high-proportion renewable energy access and provide reliable support for the safe and economic operation of the distribution network.

[0081] Correspondingly, the present application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic voltage regulation control method for the energy storage converter based on extreme value search as described above. As Figure 2 shown, it is a hardware structure diagram of any device with data processing ability where the dynamic voltage regulation control method for the energy storage converter based on extreme value search provided by the embodiment of the present invention is located. Except for Figure 2 the processors, memory and network interfaces shown, any device with data processing ability where the device in the embodiment is located usually also includes other hardware according to the actual functions of the any device with data processing ability, which will not be elaborated here.

[0082] Correspondingly, the present application further provides a computer-readable storage medium, on which computer instructions are stored. When the instructions are executed by a processor, the above-mentioned dynamic voltage regulation control method of the energy storage converter based on extreme value search is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store the data that has been output or will be output.

[0083] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the content disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only to be regarded as exemplary.

[0084] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A dynamic voltage regulation control method for an energy storage converter based on extremum seeking, characterized in that, The method includes: Constructing a partition voltage model to divide the distribution network scenario into regions, and calculating the voltage risk index corresponding to each region; When the voltage risk index corresponding to a certain region is greater than the threshold, voltage regulation is performed on this region; wherein, the process of performing voltage regulation on this region includes: Constructing a control model for the energy storage converter, the control model of the energy storage converter includes an outer-loop power control loop and an inner-loop current control loop that cooperate with each other; wherein, the outer-loop power control loop is used to output a reference voltage or reactive power according to the target voltage; the inner-loop current control loop, in response to the reference voltage output by the outer-loop power control loop, controls the voltage by adjusting the current output by the converter; Setting a voltage control objective function, and based on the control model of the energy storage converter, solving the voltage control objective function based on the extremum search algorithm to obtain the optimal reference active power and the optimal reference reactive power; According to the optimal reference active power and the optimal reference reactive power, obtain the reference value of the current inner loop.

2. The dynamic voltage regulation control method of an energy storage converter based on extremum seeking according to claim 1, wherein The process of constructing the partition voltage model includes: Divide the distribution network scenario into N regions, each region containing M i nodes; obtain the instantaneous value V i,k (t); Detect abnormal data and calculate the mean value of the effective node voltage after removing the abnormal data The expression is as follows: where M i ' is the number of valid nodes after removing anomalies, Ω is the valid node, k is the k-th valid node, and i is the i-th region; Use the exponentially weighted moving average to calculate the average voltage of the i-th region The expression is as follows: In the formula, α is the smoothing coefficient, and Δt represents the time interval.

3. A dynamic voltage regulation control method for an energy storage converter based on extremum search according to claim 1, characterized in that, The voltage risk index includes a basic risk index and a dynamic risk threshold, and the expression is as follows: In the formula, represents the basic risk indicator, R th represents the dynamic risk threshold Among them, the expression of the basic risk index is as follows: Wherein, is the root mean square error, V ref is the rated value of the voltage; Among them, the expression of the dynamic risk threshold is as follows: R th = R base + β·ΔP dg ΔP dg = |P dg (t) - P dg (t - T)| where R base is the basic threshold corresponding to the scenario without distributed power source disturbance; P dg is the output power of the distributed power source, and ΔP dg represents the power change of the distributed power source within the time window T, and β is the sensitivity coefficient.

4. A dynamic voltage regulation control method for an energy storage converter based on extremum seeking according to claim 1, characterized in that The control parameters of the outer-loop power control loop include an active power regulation channel and a reactive power regulation channel; wherein, the expression of the active power regulation channel is as follows: Wherein, P ref is the reference active power, P sch is the scheduled active power, K p is the active power ratio coefficient, H zone is the inertia time constant of the partition, indicating the inertia support ability of the energy storage and generator in this area for frequency; T droop is the droop time constant, determining the power adjustment speed; ω grid is the actual angular frequency of the power grid, ω nom is the nominal angular frequency, is the voltage risk index, ΔP vsg is the compensation power of the virtual synchronous machine, and the virtual inertia J vir simulates the rotor inertia of the synchronous machine to suppress the frequency change rate The virtual damping D vir provides a damping torque, and Δω represents the difference between the actual angular frequency and the nominal angular frequency; Further, the expression of the reactive power regulation channel is as follows: Wherein, Q ref is the reference reactive power, and Q sch is the planned reactive power, and K q is the reactive power gain coefficient; X filter is the filter reactance for suppressing the converter switching harmonics; represents the square value of the nominal voltage; S rated is the rated capacity of the converter, is the average voltage of the i-th area, V nom is the nominal voltage, V i max , V i min are respectively the upper and lower voltage limits allowed for the safe operation of the i-th area, and γ υ is the voltage risk gain.

5. A dynamic voltage regulation control method for an energy storage converter based on extreme value search according to claim 1, characterized in that, The control parameters of the inner-loop current control loop include: Wherein, i d ,i q are the d-axis / q-axis reference voltages, reference currents, and actual currents respectively, K p is the proportionality coefficient, K i is the integral coefficient, s is the Laplace operator, ω is the grid angular frequency, L is the filtering inductor, V PCC is the amplitude of the grid connection point voltage.

6. A dynamic voltage regulation control method for an energy storage converter based on extremum seeking according to claim 1, characterized in that, The process of setting a voltage control objective function, and based on the control model of the energy storage converter, solving the voltage control objective function based on the extremum search algorithm to obtain the optimal reference active power and the optimal reference reactive power includes: To the reference active power P ref and the reference reactive power Q ref inject a high-frequency sinusoidal perturbation, and the expression is as follows: μ0 = [P ref , Q ref ​ where a is the disturbance amplitude, ω p , ω q is the disturbance frequency, and it satisfies ω p ≠ ω q to decouple the active / reactive power channels; where, V i,k is the node voltage data collected in real time by the PMU or smart meter, V ref is the rated value of the voltage, S rated is the rated capacity of the converter, and λ indicates that the higher the regional voltage risk, the more priority will be given to voltage regulation; Set a voltage control objective function, and the expression is as follows:

7. A dynamic voltage regulation control method for an energy storage converter based on extremum search according to claim 1, characterized in that Based on the extremum search algorithm to solve the voltage control objective function, when the relative change amount of the voltage control objective function is less than the threshold, obtain the optimal reference active power and the optimal reference reactive power obtained by solving with the extremum search algorithm. In the formula, is the reference value of the inner current loop, represents the optimal reference active power obtained by solving with the extremum search algorithm, represents the optimal reference reactive power obtained by solving with the extremum search algorithm, V PCC represents the amplitude of the grid-connected point voltage, and j represents the imaginary number.

8. An electronic device, characterized in that, The process of obtaining the reference value of the current inner loop according to the optimal reference active power and the optimal reference reactive power includes: Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, 9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor, so that the at least one processor can execute the dynamic voltage regulation control method of the energy storage converter based on extremum search as described in any one of claims 1-7.

10. A computer program product, comprising a computer program / instructions, characterized in that, The computer program, when executed by the processor, implements the dynamic voltage regulation control method of the energy storage converter based on extremum search as described in any one of claims 1-7. When the computer program / instructions are executed by the processor, they implement the dynamic voltage regulation control method of the energy storage converter based on extremum search as described in any one of claims 1-7.

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