Energy storage grid-connected system parameter optimization method based on multi-objective genetic algorithm and related equipment

Abstract

The invention discloses an energy storage grid-connected system parameter optimization method based on a multi-objective genetic algorithm and related equipment. The method comprises the steps of obtaining an energy storage grid-connected system simulation model; determining transient stability parameter items; constructing a multi-target parameter optimization model by taking the maximum steady-state duration, the dynamic performance of the power grid and the stability margin as targets; solving the multi-target parameter optimization model in combination with the simulation model to generate a preferred parameter combination; judging whether the resonant frequency of the LCL filter of each optimal parameter combination is matched with the switching frequency or not, and if yes, deleting the corresponding optimal parameter combination; and taking a combination, which enables the degree of instability of the simulation model to be the lowest, in the remaining optimal parameter combinations as an optimal parameter combination. It can be seen that according to the method, efficient and low-risk setting of the control parameters can be achieved through simulation-driven multi-target optimization and targeted screening, the transient stability of the energy storage grid-connected system is remarkably improved, and the problems that in the prior art, manual setting is low in efficiency and high in risk are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, more particularly, to a parameter optimization method for energy storage grid-connected system based on multi-objective genetic algorithm and related equipment. BACKGROUND

[0002] With the increasing penetration of new energy, the power grid presents low inertia and weak damping characteristics, and the energy storage grid-connected system plays a more and more important role in maintaining the stability of the power grid.

[0003] However, under complex and variable power grid disturbances, the energy storage grid-connected system is prone to transient instability, which seriously affects the safe operation of the power grid. At present, after the energy storage system is put into operation, in order to cope with the possible transient instability risk, the operation and maintenance personnel mainly rely on manual and post-control parameter adjustment and setting according to the field conditions. This repeated manual debugging method accompanying the entire operation cycle of the energy storage grid-connected system has strong dependence on manpower and low efficiency. Therefore, how to provide an intelligent parameter optimization method for the energy storage grid-connected system to reduce the dependence on manpower and improve the optimization efficiency has become a technical problem to be solved in the field. SUMMARY

[0004] Therefore, the present application provides a parameter optimization method for energy storage grid-connected system based on multi-objective genetic algorithm and related equipment to solve the low efficiency of the existing parameter optimization technology for energy storage grid-connected system.

[0005] In order to achieve the above-mentioned purpose, the present scheme is as follows:

[0006] A parameter optimization method for energy storage grid-connected system based on multi-objective genetic algorithm, comprising:

[0007] simulating the energy storage grid-connected system to obtain a simulation model;

[0008] determining a plurality of transient stability parameter items in the energy storage grid-connected system;

[0009] maximizing the steady-state duration, the power grid dynamic performance and the stability margin as the target to construct a multi-objective parameter optimization model;

[0010] using a multi-objective genetic algorithm to solve the multi-objective parameter optimization model in combination with the simulation model to generate a plurality of preferred parameter combinations containing the parameter value corresponding to each transient stability parameter item;

[0011] judging whether the LCL filter resonance frequency of the energy storage grid-connected system matches the switching frequency after each preferred parameter combination is applied to the energy storage grid-connected system, and if so, deleting the corresponding preferred parameter combination;

[0012] combining the parameter combination of each of the remaining preferred parameter combinations with the lowest degree of instability of the simulation model as the optimal parameter combination.

[0013] Optionally, after combining the parameter combination of each of the remaining preferred parameter combinations with the lowest degree of instability of the simulation model as the optimal parameter combination, further comprising:

[0014] After determining that the energy storage grid-connected system is in the instability mode, fine-tuning and optimizing each of the remaining preferred parameter combinations to generate a plurality of field parameter combinations of each of the preferred parameter combinations;

[0015] Determining whether the LCL filter resonance frequency of the energy storage grid-connected system matches the switching frequency after each field parameter combination is applied to the energy storage grid-connected system, and if so, deleting the corresponding field parameter combination;

[0016] Setting the simulation model to the instability mode;

[0017] Evaluating each field parameter combination and each preferred parameter combination in combination with the set simulation model and the multi-objective parameter optimization model, and selecting the optimal parameter combination.

[0018] Optionally, the simulation of the energy storage grid-connected system to obtain a simulation model comprises:

[0019] According to the charge relationship curve and the boundary rule of the energy storage battery, an energy storage battery simulation model is constructed;

[0020] According to the dynamic characteristics of the DC side capacitor of the energy storage grid-connected system, a bidirectional converter simulation model is constructed;

[0021] The hierarchical control architecture including a phase-locked loop, a power outer loop controller, a voltage outer loop controller, and a voltage inner loop controller is simulated to obtain a hierarchical simulation model;

[0022] An electrical interface simulation model including a three-phase LCL filter simulator, a point of common coupling (PCC) measurement simulation unit, and a variable short circuit ratio (SCR) power grid equivalent model is constructed;

[0023] Based on the energy storage battery simulation model, the bidirectional converter simulation model, the hierarchical simulation model, and the electrical interface simulation model, a simulation model of the energy storage grid-connected system is generated.

[0024] Optionally, the determination of a plurality of transient stability parameter items in the energy storage grid-connected system comprises:

[0025] Determining a first parameter that affects the synchronous stability of the energy storage grid-connected system;

[0026] Determining a second parameter that affects the unity of the power loop and the voltage loop in the energy storage grid-connected system;

[0027] Determine the third parameter that affects the uniformity of the current loop and dq axis in the energy storage grid-connected system;

[0028] The hardware parameters of the LCL filter in the energy storage grid-connected system are determined to be the fourth parameter;

[0029] The first parameter, the second parameter, the third parameter, and the fourth parameter are determined as multiple transient stable parameter items.

[0030] Optionally, the construction of a multi-objective parameter optimization model with the objectives of maximizing steady-state duration, grid dynamic performance, and stability margin includes:

[0031] With the goals of maximizing steady-state duration, grid dynamic performance, and stability margin, and with the hard constraint that the energy storage converter PCS does not disconnect from the grid, and with the soft constraints that the power quality meets the preset quality requirements, the bandwidth range meets the preset bandwidth requirements, and the phase margin meets the preset phase requirements, a multi-objective parameter optimization model is constructed.

[0032] Optionally, the objective of maximizing steady-state duration, grid dynamic performance, and stability margin includes:

[0033] Identify several typical transient instability scenarios for the energy storage grid-connected system;

[0034] Construct a steady-time calculation function to calculate the steady-state duration for various typical transient instability scenarios;

[0035] Construct a performance calculation function to calculate the dynamic performance of various typical transient instability scenarios;

[0036] Construct a margin calculation function to calculate the stability margin for various typical transient instability scenarios;

[0037] The objective is to maximize the output values ​​of the steady-state time calculation function, the performance calculation function, and the margin calculation function.

[0038] Optionally, the hard constraint of the energy storage converter PCS not disconnecting from the grid, and the soft constraints of power quality meeting preset quality requirements, bandwidth range meeting preset bandwidth requirements, and phase margin meeting preset phase requirements, include:

[0039] The hard constraints are that the DC voltage does not exceed the safe voltage range and the AC current does not exceed the safe current range. The soft constraints are that the total harmonic distortion rate of the grid connection point current is lower than the preset distortion threshold, the power factor exceeds the preset factor threshold, the bandwidth range meets the preset bandwidth requirements, and the phase margin meets the preset phase requirements.

[0040] A parameter optimization device for an energy storage grid-connected system based on a multi-objective genetic algorithm, comprising:

[0041] The simulation module is used to simulate the energy storage grid-connected system and obtain a simulation model;

[0042] The determination module is used to determine multiple transient stability parameters in the energy storage grid-connected system;

[0043] The module is used to construct a multi-objective parameter optimization model with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin.

[0044] The solution module is used to solve the multi-objective parameter optimization model by employing a multi-objective genetic algorithm in conjunction with the simulation model, and to generate multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item.

[0045] The judgment module is used to determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each preferred parameter combination is applied to the energy storage grid-connected system. If so, the corresponding preferred parameter combination is deleted.

[0046] The selection module is used to select the combination of parameters that results in the lowest degree of instability in the simulation model from the remaining preferred parameter combinations as the optimal parameter combination.

[0047] A parameter optimization device for a grid-connected energy storage system based on a multi-objective genetic algorithm, comprising a memory and a processor;

[0048] The memory is used to store programs;

[0049] The processor is used to execute the program to implement each step of the above-described method for optimizing the parameters of a grid-connected energy storage system based on a multi-objective genetic algorithm.

[0050] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of the above-described method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm.

[0051] As can be seen from the above technical solutions, the energy storage grid-connected system parameter optimization method based on multi-objective genetic algorithm provided in this application can simulate the energy storage grid-connected system and obtain a simulation model. Based on this, the dynamic response of the energy storage grid-connected system can be simulated by constructing a simulation model of the energy storage grid-connected system without conducting high-risk tests on the real power grid. This application determines multiple transient stability parameters in the energy storage grid-connected system; constructs a multi-objective parameter optimization model with the goal of maximizing steady-state duration, grid dynamic performance, and stability margin; and solves the multi-objective parameter optimization model using a multi-objective genetic algorithm combined with the simulation model to generate a model containing parameters for each transient state. This approach utilizes multiple optimal parameter combinations corresponding to the steady-state parameter terms. It allows for the determination of optimal parameter values ​​for multiple core parameters by considering the dynamic response of the simulation model, integrating multiple dimensions such as steady-state duration, grid dynamic performance, and stability margin. This avoids performance imbalances caused by single-objective optimization and improves the overall stability of the energy storage grid-connected system. Steady-state duration corresponds to the long-term stable operation capability of the energy storage grid-connected system, ensuring its continued effectiveness under normal operating conditions. Dynamic performance corresponds to the short-term disturbance recovery capability of the energy storage grid-connected system, ensuring rapid return to stability during sudden grid fluctuations. Furthermore, stability margin corresponds to the system's potential to withstand unknown disturbances, enhancing its robustness against extreme conditions. Therefore, maximizing steady-state duration, grid dynamic performance, and stability margin further guarantees the adaptability and reliability of the energy storage grid-connected system in complex grid environments. Meanwhile, this application rapidly selects theoretically optimal parameter combinations by combining simulation models, reducing the cost of manual trial and error and improving parameter tuning efficiency. Based on this, this application can determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each optimal parameter combination is applied. If so, the corresponding optimal parameter combination is deleted. Thus, this application can remove parameter combinations that may lead to resonance instability from the optimal solution space, ensuring the physical realizability of the optimization results and further improving the effectiveness and reliability of the solution space, ensuring the stability of the energy storage grid-connected system from the perspective of hardware control coordination. Subsequently, this application selects the combination that minimizes the instability of the simulation model among the remaining optimal parameter combinations as the optimal parameter combination, ensuring that the finally determined optimal parameter combination can suppress transient instability to the maximum extent. It is evident that this application can achieve efficient and low-risk tuning of control parameters through simulation-driven multi-objective optimization and targeted screening, significantly improving the transient stability of the energy storage grid-connected system and solving the problems of low efficiency and high risk in manual tuning in existing technologies. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0053] Figure 1 This is a flowchart of a parameter optimization method for an energy storage grid-connected system based on a multi-objective genetic algorithm, as disclosed in an embodiment of this application.

[0054] Figure 2 This is a structural block diagram of a parameter optimization device for an energy storage grid-connected system based on a multi-objective genetic algorithm disclosed in an embodiment of this application;

[0055] Figure 3 This is a hardware structure block diagram of a parameter optimization device for an energy storage grid-connected system based on a multi-objective genetic algorithm, as disclosed in an embodiment of this application. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] This application provides a method for optimizing the parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm. This method can be applied to various energy storage management systems or grid management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0058] Next, combine Figure 1 This application provides a detailed description of the parameter optimization method for energy storage grid-connected systems based on multi-objective genetic algorithms, including the following steps:

[0059] Step S1: Simulate the energy storage grid-connected system to obtain a simulation model.

[0060] Specifically, the components of the energy storage grid-connected system, the topological relationships between different components, and the dynamic response characteristics of each component can be determined, and a simulation model of the energy storage grid-connected system can be generated on a real-time digital simulation platform.

[0061] Step S2: Determine multiple transient stability parameters in the energy storage grid-connected system.

[0062] Specifically, multiple transient stability parameters that affect the voltage response speed, current response speed, oscillation decay characteristics, stability margin, and resonance risk of the energy storage grid-connected system under transient scenarios such as grid voltage drops and load changes can be identified.

[0063] Step S3: Construct a multi-objective parameter optimization model with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin.

[0064] Specifically, the steady-state duration is quantified by the continuous duration of the rated operating state, the dynamic performance of the power grid is quantified by the weighted calculation of recovery time, overshoot and oscillation decay rate, the stability margin is quantified by phase margin or amplitude margin, a multi-objective function is established, the relevant constraint functions of transient stability parameter terms are constructed, and a multi-objective parameter optimization model is formed.

[0065] Step S4: Using a multi-objective genetic algorithm, combined with the simulation model, solve the multi-objective parameter optimization model to generate multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item.

[0066] Specifically, the non-dominated sorting genetic algorithm NSGA-II can be used to generate an initial combination based on the historical parameter combinations of the historical energy storage grid-connected system, and multiple combinations can be generated by random sampling to obtain the initial combination population;

[0067] In each iteration, the parameters of the simulation model are adjusted according to each combination in the initial population. The simulation model after parameter adjustment is simulated in real time to obtain transient response data.

[0068] The corresponding transient response data can be substituted into the relevant constraint functions of the multi-objective parameter optimization model to determine whether the corresponding combination satisfies the relevant constraint functions.

[0069] If the conditions are not met, the corresponding combination is deleted; if the conditions are met, the corresponding transient response data is substituted into the objective function of the multi-objective parameter optimization model to obtain the output value corresponding to each combination.

[0070] Based on the output value of each combination, the crowding distance of each combination is calculated, and selection, crossover and mutation are performed on each combination to update the initial combination population;

[0071] Among them, a binary tournament algorithm based on dominance level and crowding degree can be used to select each combination;

[0072] A simulated binary crossover algorithm with a crossover rate of 0.9 can be used to crossover the various combinations;

[0073] A polynomial mutation algorithm can be used to mutate each combination.

[0074] Repeat the above iterative process until the number of iterations exceeds the preset iteration threshold. The final combination in the initial combination population is the optimal parameter combination.

[0075] Step S5: After each preferred parameter combination is applied to the energy storage grid-connected system, determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, delete the corresponding preferred parameter combination.

[0076] Specifically, the simulation model can be parameter-set according to each preferred parameter combination, and the LCL filter resonant frequency of the simulation model after parameter setting can be calculated.

[0077] When the resonant frequency of the LCL filter is similar to or an integer multiple of its inherent switching frequency, the corresponding preferred parameter combination can be deleted or the capacitor value or grid-side inductance value in the corresponding preferred parameter combination can be adjusted.

[0078] The resonant frequency of the LCL filter can be calculated using the following resonant frequency calculation function:

[0079]

[0080] f res L1 is the resonant frequency of the LCL filter; C is the simulated converter-side inductance value; L2 is the simulated grid-side inductance value.

[0081] Step S6: Select the combination of parameters that results in the lowest degree of instability of the simulation model from the remaining preferred parameter combinations as the optimal parameter combination.

[0082] Specifically, the simulation model can be updated according to each preferred parameter combination, and the simulation model after parameter update can be rolled online to determine a stable feature vector containing the voltage standard deviation sequence, power fluctuation rate, maximum frequency offset, number of phase jumps and total harmonic distortion rate increment of the simulation model.

[0083] Calculate the Mahalanobis distance between the stable eigenvector and the standard eigenvector;

[0084] The optimal parameter combination can be selected based on the minimum Mahalanobis distance.

[0085] Alternatively, the simulation model can be updated with parameters for each preferred parameter combination. The simulation model after parameter updates can be rolled online to obtain the steady-state response data corresponding to each preferred parameter combination. The steady-state response data can then be substituted into the corresponding objective functions of steady-state duration, power grid dynamic performance, and stability margin. The preferred parameter combination with the best overall performance can be selected as the optimal parameter combination.

[0086] As can be seen from the above technical solutions, the energy storage grid-connected system parameter optimization method based on multi-objective genetic algorithm provided in this application can simulate the energy storage grid-connected system and obtain a simulation model. Based on this, the dynamic response of the energy storage grid-connected system can be simulated by constructing a simulation model of the energy storage grid-connected system without conducting high-risk tests on the real power grid. This application determines multiple transient stability parameters in the energy storage grid-connected system; constructs a multi-objective parameter optimization model with the goal of maximizing steady-state duration, grid dynamic performance, and stability margin; and solves the multi-objective parameter optimization model using a multi-objective genetic algorithm combined with the simulation model to generate a model containing parameters for each transient state. This approach utilizes multiple optimal parameter combinations corresponding to the steady-state parameter terms. It allows for the determination of optimal parameter values ​​for multiple core parameters by considering the dynamic response of the simulation model, integrating multiple dimensions such as steady-state duration, grid dynamic performance, and stability margin. This avoids performance imbalances caused by single-objective optimization and improves the overall stability of the energy storage grid-connected system. Steady-state duration corresponds to the long-term stable operation capability of the energy storage grid-connected system, ensuring its continued effectiveness under normal operating conditions. Dynamic performance corresponds to the short-term disturbance recovery capability of the energy storage grid-connected system, ensuring rapid return to stability during sudden grid fluctuations. Furthermore, stability margin corresponds to the system's potential to withstand unknown disturbances, enhancing its robustness against extreme conditions. Therefore, maximizing steady-state duration, grid dynamic performance, and stability margin further guarantees the adaptability and reliability of the energy storage grid-connected system in complex grid environments. Meanwhile, this application rapidly selects theoretically optimal parameter combinations by combining simulation models, reducing the cost of manual trial and error and improving parameter tuning efficiency. Based on this, this application can determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each optimal parameter combination is applied. If so, the corresponding optimal parameter combination is deleted. Thus, this application can remove parameter combinations that may lead to resonance instability from the optimal solution space, ensuring the physical realizability of the optimization results and further improving the effectiveness and reliability of the solution space, ensuring the stability of the energy storage grid-connected system from the perspective of hardware control coordination. Subsequently, this application selects the combination that minimizes the instability of the simulation model among the remaining optimal parameter combinations as the optimal parameter combination, ensuring that the finally determined optimal parameter combination can suppress transient instability to the maximum extent. It is evident that this application can achieve efficient and low-risk tuning of control parameters through simulation-driven multi-objective optimization and targeted screening, significantly improving the transient stability of the energy storage grid-connected system and solving the problems of low efficiency and high risk in manual tuning in existing technologies.

[0087] In some embodiments of this application, considering that during the entire operation cycle of the energy storage grid-connected system, changes in the real environment may lead to new instability models in the system, rendering the previously determined optimal parameter combination unable to meet transient stability requirements, the parameter values ​​of each transient stability parameter item can be updated after selecting the optimal parameter combination and detecting a new instability mode in step S6, ensuring the safe and stable operation of the energy storage grid-connected system. The above update process will be described in detail below:

[0088] S7. After determining that the energy storage grid-connected system is in an unstable mode, fine-tune and optimize each remaining preferred parameter combination to generate multiple domain parameter combinations for each preferred parameter combination.

[0089] Specifically, after detecting that the energy storage grid-connected system is in an unstable mode or after technicians report that the energy storage grid-connected system is in an unstable mode, the remaining optimal parameter combinations mentioned above can be retrieved.

[0090] Among them, the Mahalanobis distance between the stable eigenvector and the standard eigenvector of the energy storage grid-connected system can be calculated in real time. When the Mahalanobis distance exceeds 3 times the benchmark standard deviation, it is determined that the energy storage grid-connected system is in an unstable mode.

[0091] Based on the disturbance magnitude, a matching combination of domain parameters can be generated for each preferred parameter combination.

[0092] The disturbance amplitude can be 5%.

[0093] S8. After each domain parameter combination is applied to the energy storage grid-connected system, determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, delete the corresponding domain parameter combination.

[0094] Specifically, the resonant frequency of the LCL filter corresponding to each field parameter combination can be calculated, and the resonant frequency of the LCL filter of each field parameter combination can be matched with the switching frequency. If the match is successful, the corresponding field parameter combination can be deleted, or the capacitance value or grid-side inductance value in the corresponding field parameter combination can be adjusted.

[0095] S9. Set the simulation model to the instability mode.

[0096] Specifically, the grid parameters of the energy storage grid-connected system under the instability mode and the operating data for a period of time before and after the instability mode can be obtained. Based on the grid parameters and operating data, the simulation model can be set to the instability mode.

[0097] S10. Evaluate the parameter combinations and preferred parameter combinations for each domain by combining the set simulation model and the multi-objective parameter optimization model, and select the best parameter combination.

[0098] Specifically, the steady-state response data of the simulation model under each parameter combination can be substituted into the corresponding objective functions of the steady-state duration, power grid dynamic performance, and stability margin of the multi-objective parameter optimization model, and the parameter combination with the best overall performance can be selected as the optimal parameter combination.

[0099] Among them, each parameter combination can be a preferred parameter combination or a parameter combination for each field.

[0100] To ensure a smooth parameter transition, the new parameter combination can be linearly interpolated over a 10-second period.

[0101]

[0102] Where P(t) represents the parameter values ​​of each transient stability parameter in the simulation model at time t; P old Pnew represents the original parameter combination; Pnew represents the parameter combination to be switched to.

[0103] As can be seen from the above technical solution, this embodiment adds an optional process for updating the parameter values ​​of each transient stability parameter item. Through the above process, the transient stability parameter items of the energy storage grid-connected system can be adaptively evolved, ensuring the safe and stable operation of the energy storage grid-connected system throughout its entire operating cycle.

[0104] In some embodiments of this application, the process of simulating the energy storage grid-connected system to obtain a simulation model is described in detail, and the steps are as follows:

[0105] S10. Based on the charge relationship curve and boundary rules of the energy storage battery, construct a simulation model of the energy storage battery.

[0106] Specifically, a simulation model of an energy storage battery can be constructed based on the charge-discharge relationship curve of the energy storage battery and boundary rules such as charging and discharging power limits and safety limits.

[0107] S11. Based on the dynamic characteristics of the DC-side capacitor of the energy storage grid-connected system, construct a simulation model of the bidirectional converter.

[0108] Specifically, the multi-physics dynamic characteristics of the DC-side capacitor can be analyzed, including electrochemical aging, electromagnetic parasitic parameters, and coupling effects with the source and load, and a bidirectional converter simulation model integrating power device switching nonlinearity, multi-time-scale control strategies, and capacitor dynamic disturbances can be constructed.

[0109] S12. Simulate the hierarchical control architecture, which includes a phase-locked loop, a power outer loop controller, a voltage outer loop controller, and a voltage inner loop controller, to obtain a hierarchical simulation model.

[0110] Specifically, based on the phase detection delay of the phase-locked loop and its adaptability to power grid harmonics, the dynamic adjustment bandwidth and steady-state accuracy of the power outer loop controller are matched, and the anti-disturbance capability and response speed of the voltage outer loop controller and the voltage inner loop controller are coordinated, a hierarchical simulation model that can reflect the interactive influence of control levels can be constructed by introducing inter-level coupling disturbance models such as the impact of power fluctuations on the voltage loop and the interference of inner loop current ripple on the phase-locked loop.

[0111] S13. Construct an electrical interface simulation model that includes a three-phase LCL filter simulator, a grid connection point PCC measurement simulation unit, and a variable short-circuit ratio SCR power grid equivalent model.

[0112] Specifically, a three-phase LCL filter simulator can be composed of a simulated converter-side inductor, a simulated capacitor, and a simulated grid-side inductor.

[0113] The SCR range can be from 1.5 to 20.

[0114] S14. Based on the energy storage battery simulation model, the bidirectional converter simulation model, the hierarchical simulation model, and the electrical interface simulation model, generate the simulation model of the energy storage grid-connected system.

[0115] Specifically, the topological connection relationship between the energy storage battery simulation model, the bidirectional converter simulation model, the hierarchical simulation model, and the electrical interface simulation model can be set to generate the simulation model of the energy storage grid-connected system.

[0116] As can be seen from the above technical solution, this embodiment provides an optional method for constructing a simulation model of an energy storage grid-connected system. This method allows for better construction of the simulation model and improves the reliability of parameter combination selection.

[0117] In some embodiments of this application, the process of step S2, determining multiple transient stability parameter items in the energy storage grid-connected system, is described in detail as follows:

[0118] S20. Determine the first parameter that affects the synchronization stability of the energy storage grid-connected system.

[0119] Specifically, the phase-locked loop proportional gain coefficient and the phase-locked loop integral gain coefficient can be determined as the first parameters.

[0120] S21. Determine the second parameter that affects the uniformity of the power loop and voltage loop in the energy storage grid-connected system.

[0121] Specifically, the proportional gain coefficient of the active power loop, the integral gain coefficient of the active power loop, the proportional gain coefficient of the reactive power loop, the integral gain coefficient of the reactive power loop, the proportional gain coefficient of the DC voltage outer loop, and the integral gain coefficient of the DC voltage outer loop can be determined as the second parameter.

[0122] S22. Determine the third parameter that affects the uniformity of the current loop and dq axis in the energy storage grid-connected system.

[0123] Specifically, the d-axis can be a direct axis, and the q-axis can be an intersecting axis.

[0124] The proportional gain coefficient of the active current inner loop, the integral gain coefficient of the active current inner loop, the proportional gain coefficient of the reactive current inner loop, and the integral gain coefficient of the reactive current inner loop can be determined as the third parameter.

[0125] S23. Determine the hardware parameters of the LCL filter in the energy storage grid-connected system as the fourth parameter.

[0126] Specifically, the capacitor value and the grid-side inductance value in the LCL filter can be determined as the fourth parameter.

[0127] S24. The first parameter, the second parameter, the third parameter, and the fourth parameter are determined as multiple transient stable parameter items.

[0128] Specifically, each transient stability parameter may include any combination of the following: phase-locked loop proportional gain coefficient, phase-locked loop integral gain coefficient, active power loop proportional gain coefficient, active power loop integral gain coefficient, reactive power loop proportional gain coefficient, reactive power loop integral gain coefficient, DC voltage outer loop proportional gain coefficient, DC voltage outer loop integral gain coefficient, proportional gain coefficient, active current inner loop integral gain coefficient, reactive current inner loop proportional gain coefficient, reactive current inner loop integral gain coefficient, capacitance value, and grid-side inductance value.

[0129] As can be seen from the above technical solution, this embodiment provides an optional method for determining multiple transient stability parameters in the energy storage grid-connected system. This method allows for unified optimization and collaborative design of hardware and control parameters.

[0130] In some embodiments of this application, the process of constructing a multi-objective parameter optimization model with the objectives of maximizing steady-state duration, power grid dynamic performance, and stability margin is described in detail below:

[0131] S30. With the goal of maximizing steady-state duration, grid dynamic performance and stability margin, and with the hard constraint that the energy storage converter PCS does not disconnect from the grid, and with the soft constraints that the power quality meets the preset quality requirements, the bandwidth range meets the preset bandwidth requirements and the phase margin meets the preset phase requirements, a multi-objective parameter optimization model is constructed.

[0132] Specifically, it can generate objective functions for calculating steady-state duration, grid dynamic performance, and stability margin;

[0133] It can construct constraint functions for both hard and soft constraints;

[0134] By integrating the objective function and constraint functions, a multi-objective parameter optimization model is constructed.

[0135] As can be seen from the above technical solution, this embodiment provides an optional way to construct a multi-objective parameter optimization function. Through the above method, the objective function and different constraints can be combined to complete the model construction, making the generated optimal parameter combination more effective.

[0136] In some embodiments of this application, the process in step S30, which aims to maximize steady-state duration, grid dynamic performance, and stability margin, is described in detail below:

[0137] S300. Determine multiple typical transient instability scenarios of the energy storage grid-connected system.

[0138] Specifically, typical transient instability scenarios can include three-phase voltage drop scenarios, frequency step disturbance scenarios, phase jump scenarios, single-phase ground fault scenarios, weak grid switching scenarios, and load power change scenarios.

[0139] S301. Construct a steady-state time calculation function to calculate the steady-state duration of various typical transient instability scenarios.

[0140] Specifically, the steady-state time calculation function can be as follows:

[0141]

[0142] In the formula, f1(X) is the total steady-state duration corresponding to the parameter combination X; w i Let be the weight of the i-th typical transient instability scenario; denoted as , where is the continuous stable operating time of the i-th typical transient instability scenario; n is the total number of typical transient instability scenarios.

[0143] S302. Construct a performance calculation function to calculate the dynamic performance of various typical transient instability scenarios.

[0144] Specifically, the performance calculation function can be as follows:

[0145]

[0146] In the formula, f1(X) represents the dynamic performance of the power grid corresponding to the parameter combination X; Recovery time; This represents the maximum overshoot. This refers to the oscillation decay rate; , and For normalized weights.

[0147] S303. Construct a margin calculation function to calculate the stability margin for each typical transient instability scenario.

[0148] Specifically, the margin calculation function can be as follows:

[0149]

[0150] In the formula, f3(X) is the stability margin corresponding to the parameter combination X.

[0151] S304. The objective is to maximize the output values ​​of the steady-state time calculation function, the performance calculation function, and the margin calculation function.

[0152] Specifically, the objective can be to maximize the weighted sum of the output values ​​of the steady-state time calculation function, the performance calculation function, and the margin calculation function.

[0153] As can be seen from the above technical solution, this embodiment provides an optional method aimed at maximizing steady-state duration, power grid dynamic performance, and stability margin. Through this method, objective functions corresponding to different objectives can be constructed, and the output value of each objective function can be maximized as the objective.

[0154] In some embodiments of this application, the process of step S30, which uses the energy storage converter PCS not disconnecting from the grid as a hard constraint and the power quality meeting preset quality requirements, the bandwidth range meeting preset bandwidth requirements, and the phase margin meeting preset phase requirements as soft constraints, is described in detail below:

[0155] S300 sets hard constraints on DC voltage not exceeding the safe voltage range and AC current not exceeding the safe current range, and soft constraints on the total harmonic distortion rate of the grid connection point current being lower than the preset distortion threshold, the power factor exceeding the preset factor threshold, the bandwidth range meeting the preset bandwidth requirements, and the phase margin meeting the preset phase requirements.

[0156] Specifically, hard constraints can include:

[0157]

[0158]

[0159] Soft constraints can include:

[0160] THD≤5%

[0161] PF≥0.95

[0162] PM > 30°

[0163] BW inner >10×BWouter

[0164] Among them, V dc,rated This is the rated reference value for DC voltage; V dc DC voltage; I ac For alternating current; I rated The rated reference value for AC current; THD is the total harmonic distortion of the grid-connected current; PF is the power factor; PM is the phase margin; BW inner BW is the current loop bandwidth. outer This represents the power loop bandwidth.

[0165] As can be seen from the above technical solution, this embodiment provides an optional approach that uses the non-disconnection of the energy storage converter PCS as a hard constraint, and the satisfaction of preset power quality requirements, preset bandwidth requirements, and preset phase margin requirements as soft constraints. This approach can further improve the constraint effectiveness of this application.

[0166] Next, we will combine Figure 2 This application provides a detailed description of the energy storage grid-connected system parameter optimization device based on a multi-objective genetic algorithm. The energy storage grid-connected system parameter optimization device based on a multi-objective genetic algorithm described below can be compared with the energy storage grid-connected system parameter optimization method based on a multi-objective genetic algorithm described above.

[0167] See Figure 2 It can be observed that the parameter optimization device for energy storage grid-connected systems based on multi-objective genetic algorithms may include:

[0168] Simulation module 10 is used to simulate the energy storage grid-connected system and obtain a simulation model;

[0169] Module 20 is used to determine multiple transient stability parameters in the energy storage grid-connected system;

[0170] Module 30 is used to construct a multi-objective parameter optimization model with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin.

[0171] The solution module 40 is used to solve the multi-objective parameter optimization model by employing a multi-objective genetic algorithm in conjunction with the simulation model, and to generate multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item.

[0172] The judgment module 50 is used to determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each preferred parameter combination is applied to the energy storage grid-connected system. If so, the corresponding preferred parameter combination is deleted.

[0173] Module 60 is selected to take the combination of parameters that results in the lowest degree of instability of the simulation model from the remaining preferred parameter combinations as the optimal parameter combination.

[0174] Furthermore, the energy storage grid-connected system parameter optimization device based on a multi-objective genetic algorithm may also include:

[0175] The domain parameter combination generation module is used to fine-tune and optimize each remaining preferred parameter combination after determining that the energy storage grid-connected system is in an unstable mode, and generate multiple domain parameter combinations for each preferred parameter combination.

[0176] The frequency matching module is used to determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each domain parameter combination is applied to the energy storage grid-connected system. If so, the corresponding domain parameter combination is deleted.

[0177] The simulation model setting module is used to set the simulation model to the instability mode;

[0178] The optimal parameter combination selection module is used to evaluate the parameter combinations and preferred parameter combinations in various fields by combining the set simulation model and the multi-objective parameter optimization model, and select the optimal parameter combination.

[0179] Furthermore, the simulation module 10 may include:

[0180] The first simulation unit is used to construct a simulation model of the energy storage battery based on the charge relationship curve and boundary rules of the energy storage battery.

[0181] The second simulation unit is used to construct a bidirectional converter simulation model based on the dynamic characteristics of the DC-side capacitor of the energy storage grid-connected system.

[0182] The third simulation unit is used to simulate the hierarchical control architecture, which includes a phase-locked loop, a power outer loop controller, a voltage outer loop controller, and a voltage inner loop controller, to obtain a hierarchical simulation model.

[0183] The fourth simulation unit is used to construct an electrical interface simulation model that includes a three-phase LCL filter simulator, a grid connection point PCC measurement simulation unit, and a variable short-circuit ratio SCR power grid equivalent model.

[0184] The fifth simulation unit is used to generate a simulation model of the energy storage grid-connected system based on the energy storage battery simulation model, the bidirectional converter simulation model, the hierarchical simulation model, and the electrical interface simulation model.

[0185] Furthermore, module 20 may include:

[0186] The first determining unit is used to determine a first parameter that affects the synchronization stability of the energy storage grid-connected system;

[0187] The second determining unit is used to determine a second parameter that affects the uniformity of the power loop and voltage loop in the energy storage grid-connected system;

[0188] The third determining unit is used to determine the third parameter that affects the uniformity of the current loop and dq axis in the energy storage grid-connected system.

[0189] The fourth determining unit is used to determine the hardware parameters of the LCL filter in the energy storage grid-connected system as the fourth parameter;

[0190] The fifth determining unit is used to determine the first parameter, the second parameter, the third parameter, and the fourth parameter as multiple transient stable parameter items.

[0191] Furthermore, the building module 30 may include:

[0192] The multi-objective parameter optimization model construction unit is used to construct a multi-objective parameter optimization model with the objectives of maximizing steady-state duration, grid dynamic performance and stability margin, with the hard constraint that the energy storage converter PCS does not disconnect from the grid, and the soft constraints that the power quality meets the preset quality requirements, the bandwidth range meets the preset bandwidth requirements and the phase margin meets the preset phase requirements.

[0193] Furthermore, the building blocks for multi-objective parameter optimization models may include:

[0194] The scenario determination subunit is used to determine multiple typical transient instability scenarios of the energy storage grid-connected system;

[0195] The steady-time calculation function construction sub-unit is used to construct a steady-time calculation function for calculating the steady-state duration of various typical transient instability scenarios.

[0196] The performance calculation function construction subunit is used to construct performance calculation functions for calculating the dynamic performance of various typical transient instability scenarios.

[0197] The margin calculation function construction sub-unit is used to construct the margin calculation function for calculating the stability margin of various typical transient instability scenarios;

[0198] The objective determination subunit is used to maximize the output values ​​of the steady-state time calculation function, the performance calculation function, and the margin calculation function.

[0199] Furthermore, the multi-objective parameter optimization model building unit may also include:

[0200] The soft constraint determination sub-unit is used to set hard constraints such as DC voltage not exceeding the safe voltage range and AC current not exceeding the safe current range, and soft constraints such as the total harmonic distortion rate of the grid connection point current being lower than the preset distortion threshold, the power factor exceeding the preset factor threshold, the bandwidth range meeting the preset bandwidth requirements, and the phase margin meeting the preset phase requirements.

[0201] The energy storage grid-connected system parameter optimization device based on a multi-objective genetic algorithm provided in this application embodiment can be applied to energy storage grid-connected system parameter optimization equipment based on a multi-objective genetic algorithm, such as PC terminals, cloud platforms, servers, and server clusters. Optionally, Figure 3 The hardware structure block diagram of the energy storage grid-connected system parameter optimization device based on multi-objective genetic algorithm is shown. (Refer to...) Figure 3 The hardware structure of the energy storage grid-connected system parameter optimization device based on multi-objective genetic algorithm may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0202] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0203] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0204] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0205] The memory stores a program, which the processor can call. The program is used for:

[0206] A simulation model of the energy storage grid-connected system was obtained.

[0207] Determine multiple transient stability parameters in the energy storage grid-connected system;

[0208] A multi-objective parameter optimization model is constructed with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin.

[0209] A multi-objective genetic algorithm is used in conjunction with the simulation model to solve the multi-objective parameter optimization model, generating multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item;

[0210] After each preferred parameter combination is applied to the energy storage grid-connected system, it is determined whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, the corresponding preferred parameter combination is deleted.

[0211] The combination that results in the lowest degree of instability in the simulation model among the remaining preferred parameter combinations is taken as the optimal parameter combination.

[0212] Optionally, the refined and extended functions of the program can be referred to the above description.

[0213] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for:

[0214] A simulation model of the energy storage grid-connected system was obtained.

[0215] Determine multiple transient stability parameters in the energy storage grid-connected system;

[0216] A multi-objective parameter optimization model is constructed with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin.

[0217] A multi-objective genetic algorithm is used in conjunction with the simulation model to solve the multi-objective parameter optimization model, generating multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item;

[0218] After each preferred parameter combination is applied to the energy storage grid-connected system, it is determined whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, the corresponding preferred parameter combination is deleted.

[0219] The combination that results in the lowest degree of instability in the simulation model among the remaining preferred parameter combinations is taken as the optimal parameter combination.

[0220] Optionally, the refined and extended functions of the program can be referred to the above description.

[0221] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0222] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0223] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments of this application can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A parameter optimization method for an energy storage grid-connected system based on a multi-objective genetic algorithm, characterized in that, include: A simulation model of the energy storage grid-connected system was obtained. Determine multiple transient stability parameters in the energy storage grid-connected system; A multi-objective parameter optimization model is constructed with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin. A multi-objective genetic algorithm is used in conjunction with the simulation model to solve the multi-objective parameter optimization model, generating multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item; After each preferred parameter combination is applied to the energy storage grid-connected system, it is determined whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, the corresponding preferred parameter combination is deleted. The combination that results in the lowest degree of instability in the simulation model among the remaining preferred parameter combinations is taken as the optimal parameter combination.

2. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 1, characterized in that, After selecting the combination of parameters that minimizes the instability of the simulation model from the remaining preferred parameter combinations as the optimal parameter combination, the following is also included: After determining that the energy storage grid-connected system is in an unstable mode, fine-tuning and optimization are performed on each remaining preferred parameter combination to generate multiple domain parameter combinations for each preferred parameter combination; After each domain parameter combination is applied to the energy storage grid-connected system, it is determined whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency. If so, the corresponding domain parameter combination is deleted. Set the simulation model to the instability mode; By combining the established simulation model and the multi-objective parameter optimization model, the parameter combinations for each domain and the preferred parameter combinations are evaluated, and the optimal parameter combination is selected.

3. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 1, characterized in that, The simulation of the energy storage grid-connected system, to obtain a simulation model, includes: Based on the charge relationship curve and boundary rules of the energy storage battery, a simulation model of the energy storage battery is constructed. Based on the dynamic characteristics of the DC-side capacitor of the energy storage grid-connected system, a simulation model of the bidirectional converter is constructed. A hierarchical control architecture, including a phase-locked loop, a power outer loop controller, a voltage outer loop controller, and a voltage inner loop controller, is simulated to obtain a hierarchical simulation model. An electrical interface simulation model was constructed, which includes a three-phase LCL filter simulator, a grid connection point PCC measurement simulation unit, and a variable short-circuit ratio SCR power grid equivalent model. Based on the energy storage battery simulation model, the bidirectional converter simulation model, the hierarchical simulation model, and the electrical interface simulation model, a simulation model of the energy storage grid-connected system is generated.

4. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 1, characterized in that, The determination of multiple transient stability parameters in the energy storage grid-connected system includes: Determine the first parameter that affects the synchronization stability of the energy storage grid-connected system; Determine a second parameter that affects the uniformity of the power loop and voltage loop in the energy storage grid-connected system; Determine the third parameter that affects the uniformity of the current loop and dq axis in the energy storage grid-connected system; The hardware parameters of the LCL filter in the energy storage grid-connected system are determined to be the fourth parameter; The first parameter, the second parameter, the third parameter, and the fourth parameter are determined as multiple transient stable parameter items.

5. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 1, characterized in that, The multi-objective parameter optimization model, which aims to maximize steady-state duration, grid dynamic performance, and stability margin, includes: With the goals of maximizing steady-state duration, grid dynamic performance, and stability margin, and with the hard constraint that the energy storage converter PCS does not disconnect from the grid, and with the soft constraints that the power quality meets the preset quality requirements, the bandwidth range meets the preset bandwidth requirements, and the phase margin meets the preset phase requirements, a multi-objective parameter optimization model is constructed.

6. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 5, characterized in that, The goal of maximizing steady-state duration, grid dynamic performance, and stability margin includes: Identify several typical transient instability scenarios for the energy storage grid-connected system; Construct a steady-time calculation function to calculate the steady-state duration for various typical transient instability scenarios; Construct a performance calculation function to calculate the dynamic performance of various typical transient instability scenarios; Construct a margin calculation function to calculate the stability margin for various typical transient instability scenarios; The objective is to maximize the output values ​​of the steady-state time calculation function, the performance calculation function, and the margin calculation function.

7. The method for optimizing parameters of an energy storage grid-connected system based on a multi-objective genetic algorithm according to claim 5, characterized in that, The hard constraint is that the energy storage converter PCS does not disconnect from the grid, and the soft constraints are that the power quality meets the preset quality requirements, the bandwidth range meets the preset bandwidth requirements, and the phase margin meets the preset phase requirements. The hard constraints are that the DC voltage does not exceed the safe voltage range and the AC current does not exceed the safe current range. The soft constraints are that the total harmonic distortion rate of the grid connection point current is lower than the preset distortion threshold, the power factor exceeds the preset factor threshold, the bandwidth range meets the preset bandwidth requirements, and the phase margin meets the preset phase requirements.

8. A parameter optimization device for an energy storage grid-connected system based on a multi-objective genetic algorithm, characterized in that, include: The simulation module is used to simulate the energy storage grid-connected system and obtain a simulation model; The determination module is used to determine multiple transient stability parameters in the energy storage grid-connected system; The module is used to construct a multi-objective parameter optimization model with the goal of maximizing steady-state duration, power grid dynamic performance, and stability margin. The solution module is used to solve the multi-objective parameter optimization model by employing a multi-objective genetic algorithm in conjunction with the simulation model, and to generate multiple optimal parameter combinations containing the parameter values ​​corresponding to each transient stable parameter item. The judgment module is used to determine whether the resonant frequency of the LCL filter in the energy storage grid-connected system matches the switching frequency after each preferred parameter combination is applied to the energy storage grid-connected system. If so, the corresponding preferred parameter combination is deleted. The selection module is used to select the combination of parameters that results in the lowest degree of instability in the simulation model from the remaining preferred parameter combinations as the optimal parameter combination.

9. A parameter optimization device for an energy storage grid-connected system based on a multi-objective genetic algorithm, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the parameter optimization method for energy storage grid-connected system based on multi-objective genetic algorithm as described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the parameter optimization method for energy storage grid-connected system based on multi-objective genetic algorithm as described in any one of claims 1-7.

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