Inertia, frequency, voltage and damping multi-objective coordinated control parameter optimization method for high-voltage direct-hanging network type energy storage

Abstract

The application discloses a high-voltage direct-hanging network type energy storage control parameter optimization method for inertia, frequency, voltage and damping multi-target collaborative regulation. Quantitative indexes for characterizing inertia, frequency, voltage and system damping characteristics of the high-voltage direct-hanging network type energy storage are determined; a mathematical model for representing four types of support performance is constructed, key control parameters affecting each support performance are selected based on the mathematical model, and a quantitative mapping relationship between the key control parameters and each quantitative index is established; the collaborative / constraining influence law of each control parameter on the multi-support performance indexes is analyzed, and on this basis, a multi-dimensional feasible region of the control parameters is constructed in combination with multi-support performance constraints; a scene-driven index weight distribution mechanism is established according to the difference in the demand of the system for inertia, frequency, voltage support and damping improvement under different operation scenes; and the multi-dimensional feasible region of the control parameters is taken as a search space, and multi-target collaborative optimization is carried out in combination with the index weight, so that an optimal control parameter combination suitable for the current scene is obtained.

Description

Technical Field

[0001] This invention belongs to the field of power electronics and energy storage technology, and in particular relates to a method for optimizing control parameters of high-voltage direct-connected grid-type energy storage with multi-objective coordinated control of inertia, frequency, voltage and damping. Background Technology

[0002] As the penetration rate of new energy sources in the power system continues to increase, the system is gradually exhibiting operating characteristics of low inertia, weak damping, and strong power electronics. High-voltage direct-connected grid-connected energy storage can reduce intermediate voltage boosting links and features fast response speed, strong bidirectional power regulation capability, and flexible control. It also has capabilities such as inertia support, primary frequency regulation, voltage support, and damping improvement, and has gradually become an important technical means to improve the safety and stability of new energy grid-connected systems.

[0003] However, the active support capability of grid-connected energy storage is closely related to its control parameters. Different control parameters have varying effects on different performance indicators, and adjusting some parameters may improve one indicator while weakening another. Therefore, parameter tuning is a coupled optimization problem involving multiple parameters, indicators, and constraints. Existing methods often focus on parameter tuning for a single control objective, lacking quantitative analysis and characterization of the relationships between control parameters and multiple support performance indicators such as inertia, frequency, voltage, and damping. They also rarely consider the synergistic / constraint relationships among multiple indicators. Furthermore, different application scenarios have varying requirements for the support capability of grid-connected energy storage, making traditional empirical tuning strategies unsuitable for complex operating conditions. Therefore, there is an urgent need to propose a control parameter optimization method for high-voltage direct-connected grid-connected energy storage. This method should construct a multi-dimensional feasible domain for control parameters while satisfying multiple performance constraints, and conduct multi-objective collaborative optimization based on the needs of the operating scenario to enhance the active support capability of grid-connected energy storage for new energy grid-connected systems.

[0004] To address the aforementioned issues, this patent proposes a method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the coordinated control of multiple objectives, including inertia, frequency, voltage, and damping. This method can provide theoretical support for the tuning of control parameters and the coordinated optimization of multi-support performance in high-voltage direct-connected grid-type energy storage. Summary of the Invention

[0005] This disclosure provides a method for constructing a transient power security domain model for high-voltage direct-connected grid-type energy storage that considers electro-thermal limit constraints.

[0006] The purpose of this disclosure is achieved through the following technical solution: A method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on multi-objective coordinated control of inertia, frequency, voltage, and damping, characterized in that the method includes the following steps: S1. Construct a multi-objective quantitative index system for inertia support, frequency support, voltage support and damping characteristics; S2. Construct a mathematical model of the multi-support characteristics of grid-type energy storage and determine key control parameters; S3. Clarify the coupling influence of the key control parameters on the performance indicators of multiple supports; S4. Construct the multidimensional feasible domain of the key control parameters; S5. Establish a scenario-driven multi-support performance indicator weight allocation mechanism; S6. Conduct multi-objective collaborative optimization and output the optimal parameter combination.

[0007] Furthermore, the multi-objective quantitative index system includes the frequency change rate d. f / d t Frequency deviation Δ f The inertia support characteristic is used to characterize the energy storage's rapid response capability to the initial stage of system frequency changes, the frequency support characteristic is used to characterize the ability to participate in primary frequency regulation and frequency recovery, the voltage support characteristic is used to characterize the support capability for bus voltage stability and reactive power regulation, and the damping characteristic is used to characterize the ability to suppress oscillations and improve system dynamic stability.

[0008] Furthermore, the multi-support characteristic mathematical model includes a system frequency response aggregation model, a quasi-steady-state voltage mathematical model, and a small-signal model.

[0009] Furthermore, the frequency response aggregation model is used to reflect the inertia frequency response characteristics of the system, and its expression is as follows: Where: Δ P L、Δ P bess、Δ P d represents the load power response, energy storage power response, and system disturbance power, respectively, and Δ f For frequency deviation, As the reference power, H sys D sys k 1 represents the system inertial time constant, system damping coefficient, and load frequency response coefficient, respectively. The key control parameter affecting frequency support characteristics, as determined by the above model, is the virtual inertia coefficient. T j Damping coefficient D 1 and active-frequency droop factor D p Further obtain the relationship between each control parameter and the frequency change rate d f / d t Frequency deviation Δ fQuantitative correspondence between quantitative indicators: Furthermore, the quasi-steady-state voltage mathematical model is used to reflect the system voltage support characteristics, and it includes line equations and reactive power-voltage control equations for grid-type energy storage, as follows: Where: ΔU is the voltage deviation, ΔQ is the reactive power deviation, and X is the equivalent line reactance from energy storage to the infinite bus. U N The rated voltage at the PCC point; in: D v This is the voltage droop factor. D q is the reactive power droop factor, and K is the reactive power-voltage loop proportional coefficient.

[0010] Furthermore, the small-signal model is used to reflect the damping characteristics of the system, and its construction process is as follows: A set of nonlinear differential equations is established based on the dynamic mathematical model of each component of grid-type energy storage: Where: X is a vector composed of all state variables, and U is a vector composed of all input variables; Linearizing the above nonlinear mathematical equations at the steady-state operating point yields the small-signal model of the system: Where: A is the state matrix, and B is the input matrix; Calculate the eigenvalues ​​of state matrix A l i Obtain the damping ratio corresponding to all eigenvalues: in: e i The damping ratio for the i-th eigenvalue. s i Let be the real part of the i-th eigenvalue. oh i The imaginary part of the i-th eigenvalue; The state matrix A contains information on the control parameters of the grid-type energy storage. The relative sensitivity of each control parameter to the minimum damping ratio of the system is calculated. Based on the sensitivity, the key control parameters that affect the damping characteristics are selected, thereby establishing a quantitative correspondence between the key control parameters and the system damping index.

[0011] Furthermore, the coupling effect law is determined according to the following process: Based on the mapping relationship between the key control parameters and each quantitative index, each key control parameter is taken as the adjustment object, while keeping the other control parameters unchanged. The value of the control parameter is changed within a preset value range, and the corresponding quantitative indexes such as frequency change rate, frequency voltage deviation, and minimum damping ratio are calculated. The preset value range is jointly determined by the allowable range of controller parameters, the operating constraints of the energy storage device, and the value range of engineering experience. Based on the direction and magnitude of the change of each quantitative index with the parameter, the individual effect and multi-characteristic synergistic / constraint relationship of the parameter on inertia, frequency, voltage support, and damping characteristics are determined, thereby clarifying the coupling effect law of each control parameter on the multi-support performance index.

[0012] Furthermore, the specific construction process of the multidimensional feasible domain of the control parameters includes: The virtual inertia coefficient is determined based on the allowable range of controller parameters, the operating constraints of the energy storage device, and the range of values ​​obtained from engineering experience. T j Damping coefficient D 1. Active power-frequency droop factor D p reactive power-voltage loop ratio D v / D q And the initial range of values ​​for the reactive power-voltage loop proportional coefficient K; For each key control parameter, set a reasonable value step size and generate a set of parameter combinations; Calculate the corresponding frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation, and minimum damping ratio; Iterate through all parameter combinations and verify whether each combination simultaneously satisfies the relevant constraints of quantitative indicators for inertia, frequency, voltage, and damping characteristics. Parameter combinations that satisfy all constraints are marked as feasible parameter combinations. The key control parameters are used to construct a control parameter space. All feasible parameters are combined in the control parameter space for set representation, resulting in a five-dimensional feasible domain of control parameters.

[0013] Furthermore, the aforementioned indicator weight allocation mechanism includes: The typical application scenarios for grid-type energy storage access systems are identified, including high-proportion renewable energy transmission scenarios, DC near-field scenarios, microgrids, and other scenarios that require active support from energy storage. The degree of active support demand is determined based on the current operating condition parameters, which include one or more of the following: the proportion of new energy output, system frequency deviation, PCC point voltage deviation, DC transmission power, short-circuit ratio, and oscillation mode damping ratio. The weighting coefficients of each quantitative indicator are determined based on the degree of active support demand, wherein the higher the degree of support demand, the greater the weighting coefficient of the quantitative indicator. Normalize each weight coefficient to ensure that it satisfies the non-negativity constraint and the normalization constraint.

[0014] Furthermore, the specific process of the multi-objective collaborative optimization is as follows: Using the frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation, and minimum modal damping ratio as optimization evaluation indicators, and the multidimensional feasible region of the control parameters as the search space, a scenario-adaptive weighted comprehensive objective function is constructed by combining the weight coefficients of each indicator. The weighted comprehensive objective function takes minimizing the frequency change rate, minimizing the quasi-steady-state frequency deviation, minimizing the PCC point voltage deviation, and maximizing the minimum modal damping ratio as optimization directions. Among them, the objective of maximizing the minimum modal damping ratio is transformed into the objective of minimizing the damping performance penalty term, so that each optimization objective participates in the calculation of the comprehensive objective function. Within the multidimensional feasible region of the control parameters, the combination of key control parameters is iteratively optimized to obtain the optimal combination of grid-type energy storage control parameters that meets the support requirements of the current scenario.

[0015] By establishing a quantitative mapping relationship between control parameters and multi-support performance indicators, this study analyzes the synergistic / constraint effects of control parameters on inertia, frequency, voltage support, and system damping characteristics. Under multi-support performance constraints, a multi-dimensional feasible domain for control parameters is constructed. Combined with operational scenario requirements, multi-objective collaborative optimization is carried out to obtain the optimal combination of control parameters, achieving scenario-adaptive control parameter optimization configuration. This provides a method for tuning control parameters and coordinating multi-support performance for high-voltage direct-connected grid-type energy storage under complex operating conditions, and can provide technical guidance for the parameter design and engineering commissioning of high-voltage direct-connected grid-type energy storage controllers. Attached Figure Description

[0016] The accompanying drawings illustrate one embodiment of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0017] Figure 1 This is the overall flowchart of the high-voltage direct-connected grid-type energy storage control parameter optimization method of the present invention, which is oriented towards multi-objective coordinated control of inertia, frequency, voltage and damping. Figure 2 This is a block diagram of the frequency response aggregation model of the present invention. Detailed Implementation

[0018] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0019] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] like Figure 1 As shown, the method for constructing and optimizing the feasible domain of system control parameters according to the present invention specifically includes the following steps: S1. Construct a multi-objective quantitative index system for inertia, frequency, voltage, and damping; To address the roles of high-voltage direct-connected grid-connected energy storage in power systems, including its inertia support, frequency support, voltage support, and damping regulation, quantitative indicators reflecting the corresponding dynamic characteristics are selected, including the rate of frequency change d. f / d t Frequency deviation Δ f Based on the voltage deviation ΔU and the system damping ratio ε, a multi-objective collaborative optimization evaluation index system is constructed.

[0021] Among them, the inertia support index is used to characterize the energy storage's ability to respond quickly to the initial stage of system frequency changes, the frequency support index is used to characterize its ability to participate in primary frequency regulation and frequency recovery, the voltage support index is used to characterize its ability to support bus voltage stability and reactive power regulation, and the damping characteristic index is used to characterize its ability to suppress oscillations and improve system dynamic stability.

[0022] S2. Construct a mathematical model of the multi-support characteristics of grid-type energy storage and determine key control parameters; Based on the dominant control links of high-voltage direct-connected grid-type energy storage in inertia support, frequency support, voltage support and damping regulation, mathematical models corresponding to the four types of characteristics are established respectively. On this basis, key control parameters affecting each characteristic are screened, and the mapping relationship between control parameters and each quantitative index is obtained.

[0023] The multi-support characteristic mathematical model described in step two includes a system frequency response aggregation model, a quasi-steady-state voltage mathematical model, and a small-signal model. The frequency response aggregation model reflects the system's inertia frequency response characteristics, and its block diagram is shown below. Figure 2 As shown, the expression is as follows: Where: Δ P L ΔP bess Δ P d These are the load power response, energy storage power response, and system disturbance power, Δ. f For frequency deviation, As the reference power, H sys , D sys , k 1 represents the system inertial time constant, system damping coefficient, and load frequency response coefficient.

[0024] The key control parameter affecting frequency support characteristics, as determined by the above model, is the virtual inertia coefficient. T j Damping coefficient D 1 and active-frequency droop factor D p Further obtain the relationship between each control parameter and the frequency change rate d f / d t Frequency deviation Δ f Quantitative correspondence between quantitative indicators: The quasi-steady-state voltage mathematical model reflecting the system's voltage support characteristics includes line equations and reactive power-voltage control equations for grid-type energy storage, as detailed below: Where: Δ U For voltage deviation, Δ Q This is reactive power deviation. X The equivalent line reactance for storing energy to an infinite busbar, U N This is the rated voltage at the PCC point.

[0025] in: D v This is the voltage droop factor. D q This is the reactive power droop factor. K This is the reactive power-voltage loop proportional coefficient.

[0026] The above equations clearly show that the key control parameter affecting voltage support characteristics is the reactive power-voltage loop coefficient ratio. D v / D q and reactive power-voltage loop proportional coefficient K And reveal the relationship between each control parameter and the voltage deviation Δ U Quantitative correspondence between quantitative indicators.

[0027] The process for constructing a small-signal model that reflects the damping characteristics of the system is as follows: (1) Establish a set of nonlinear differential equations based on the dynamic mathematical model of each link of the grid-type energy storage: in: X It is a vector consisting of all state variables. U It is a vector consisting of all input variables.

[0028] (2) Linearize the above nonlinear mathematical equations at the steady-state operating point to obtain the small-signal model of the system: in: A The state matrix, B The input matrix is ​​denoted as .

[0029] (3) Calculate the state matrix A eigenvalues l i Obtain the damping ratio corresponding to all eigenvalues: in: e i The damping ratio for the i-th eigenvalue. s i Let be the real part of the i-th eigenvalue. oh i It represents the imaginary part of the i-th eigenvalue.

[0030] As can be seen from the above model, the state matrix A The data includes information on various control parameters of the grid-type energy storage system. To clarify the influence of each parameter on the system's damping characteristics, the eigenvalue sensitivity analysis method is used to calculate the relative sensitivity of each control parameter to the system's minimum damping ratio. Based on the sensitivity, key control parameters affecting the damping characteristics are selected, thereby establishing a quantitative correspondence between key control parameters and system damping indicators.

[0031] S3. Clarify the coupling influence of control parameters on the performance indicators of multiple supports; Based on the mapping relationship between the key control parameters and various quantitative indicators established in step S2, each key control parameter is adjusted while keeping the other control parameters constant. The value of this control parameter is changed within a preset range, and the corresponding quantitative indicators such as the rate of change of frequency, frequency-voltage deviation, and minimum damping ratio are calculated. The preset range is determined jointly by the allowable range of the controller parameters, the operating constraints of the energy storage device, and the range of values ​​derived from engineering experience.

[0032] Based on the direction and magnitude of the change of each quantitative index with respect to the parameter, the individual effect of the parameter on inertia, frequency, voltage support and damping characteristics and the synergistic / constraining relationship of multiple characteristics are determined, thereby clarifying the coupling influence law of each control parameter on the performance index of multiple supports.

[0033] S4. Construct a multidimensional feasible region for control parameters; Taking into account the relevant constraints of the quantitative indicators described in S1, and combining the individual effects and synergistic / constraint relationships of the control parameters provided in S3 on multiple performance indicators, the parameter combinations within the initial parameter space determined by the preset value range are constrained and verified. The parameter combinations that simultaneously satisfy the constraints of each quantitative indicator are determined as feasible parameter combinations, thereby constructing a multidimensional feasible domain for control parameters.

[0034] The relevant constraints for the quantitative indicators of inertia, frequency, voltage support, and damping characteristics are defined as follows: in: This is the limit for the system frequency change rate.

[0035] Where: Δ f max This is the limit for the quasi-steady-state frequency deviation of the system.

[0036] Where: Δ U max This is the limit for the voltage deviation at the PCC point in the quasi-steady state of the system.

[0037] in: e min This represents the minimum modal damping ratio of the system. e cri To set the damping ratio threshold.

[0038] The specific construction process of the multidimensional feasible domain of the control parameters includes: (1) Determine the virtual inertia coefficient based on the allowable range of controller parameters, the operating constraints of the energy storage device, and the range of values ​​obtained from engineering experience. T j Damping coefficient D 1. Active power-frequency droop factor D p reactive power-voltage loop ratio D v / D q and reactive power-voltage loop proportional coefficient K The initial range of values; (2) Set reasonable value step sizes for each key control parameter and generate a set of parameter combinations; (3) Substitute each parameter combination into the quantitative relationship equation established in step S2 to calculate the corresponding frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation and minimum damping ratio. (4) Traverse all parameter combinations, check each combination to see if it satisfies all four types of characteristic constraints, and mark the parameter combinations that satisfy all constraints as feasible parameter combinations; (5) The control parameter space is constructed using the five key control parameters. All feasible parameters are combined in the control parameter space for set representation to obtain the five-dimensional feasible domain of the control parameters.

[0039] S5. Establish a scenario-driven multi-support performance indicator weight allocation mechanism; Based on the operating scenarios and current conditions of the grid-connected energy storage system, the system's requirements for inertia, frequency, voltage support, and damping characteristics are determined, and weight coefficients are assigned to the corresponding quantitative indicators, forming a multi-objective weight allocation mechanism for different operating conditions. The weight coefficient assignment process includes: (1) Determine the typical application scenarios of grid-type energy storage access systems, including high-proportion new energy transmission scenarios, DC near-field scenarios, microgrids and other scenarios that require active support from energy storage; (2) Determine the degree of active support demand based on the current operating condition parameters, including one or more of the following: the proportion of new energy output, system frequency deviation, PCC point voltage deviation, DC transmission power, short-circuit ratio, and oscillation mode damping ratio; (3) Determine the weight coefficient of each quantitative indicator based on the degree of active support demand, wherein the higher the degree of support demand, the greater the weight coefficient of the corresponding quantitative indicator. (4) Normalize each weight coefficient to ensure that each weight coefficient satisfies the non-negativity constraint and the normalization constraint.

[0040] S6. Conduct multi-objective collaborative optimization and output the optimal parameter combination; Using the frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation, and minimum modal damping ratio determined in step S1 as optimization evaluation indicators, the multidimensional feasible domain of control parameters constructed in step S4 as the search space, and combined with the weight coefficients of each indicator determined in step S5, a scenario-adaptive weighted comprehensive objective function is constructed.

[0041] The weighted comprehensive objective function aims to minimize the rate of change of frequency, the deviation of quasi-steady-state frequency, the deviation of voltage at PCC point, and the maximization of the minimum modal damping ratio. The objective of maximizing the minimum modal damping ratio is transformed into the objective of minimizing the damping performance penalty term, so that all optimization objectives participate in the calculation of the comprehensive objective function.

[0042] Within the multidimensional feasible domain of the control parameters, a multi-objective optimization algorithm is used to iteratively optimize the combination of key control parameters to obtain the optimal combination of grid-type energy storage control parameters that meets the support requirements of the current scenario. The multi-objective optimization algorithm includes one of the following: particle swarm optimization, genetic algorithm, differential evolution algorithm, or an improved version thereof.

[0043] Furthermore, the optimal control parameter combination obtained was verified based on an electromagnetic transient simulation platform to verify its effectiveness in improving inertia, frequency, voltage support, and damping.

[0044] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0045] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on multi-objective coordinated control of inertia, frequency, voltage, and damping, characterized in that, The method includes the following steps: S1. Construct a multi-objective quantitative index system for inertia support, frequency support, voltage support and damping characteristics; S2. Construct a mathematical model of the multi-support characteristics of grid-type energy storage and determine key control parameters; S3. Clarify the coupling influence of the key control parameters on the performance indicators of multiple supports; S4. Construct the multidimensional feasible domain of the key control parameters; S5. Establish a scenario-driven multi-support performance indicator weight allocation mechanism; S6. Conduct multi-objective collaborative optimization and output the optimal parameter combination.

2. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 1, is characterized in that... The multi-objective quantitative index system includes the frequency change rate d. f / d t Frequency deviation Δ f The inertia support characteristic is used to characterize the energy storage's rapid response capability to the initial stage of system frequency changes, the frequency support characteristic is used to characterize the ability to participate in primary frequency regulation and frequency recovery, the voltage support characteristic is used to characterize the support capability for bus voltage stability and reactive power regulation, and the damping characteristic is used to characterize the ability to suppress oscillations and improve system dynamic stability.

3. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 2, is characterized in that... The multi-support characteristic mathematical model includes a system frequency response aggregation model, a quasi-steady-state voltage mathematical model, and a small-signal model.

4. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the coordinated control of multiple objectives including inertia, frequency, voltage, and damping, as described in claim 3, is characterized in that... The frequency response aggregation model is used to reflect the inertial frequency response characteristics of the system, and its expression is as follows: Where: Δ P L、Δ P bess、Δ P d represents the load power response, energy storage power response, and system disturbance power, respectively, and Δ f For frequency deviation, As the reference power, H sys D sys k 1 represents the system inertial time constant, system damping coefficient, and load frequency response coefficient, respectively. The key control parameter affecting frequency support characteristics, as determined by the above model, is the virtual inertia coefficient. T j Damping coefficient D 1 and active-frequency droop factor D p Further obtain the relationship between each control parameter and the frequency change rate d f / d t Frequency deviation Δ f Quantitative correspondence between quantitative indicators: 。 5. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage as described in claim 4, characterized in that: The quasi-steady-state voltage mathematical model is used to reflect the voltage support characteristics of the system. It includes line equations and reactive power-voltage control equations for grid-type energy storage, as detailed below: Where: ΔU is the voltage deviation, ΔQ is the reactive power deviation, and X is the equivalent line reactance from energy storage to the infinite bus. U N The rated voltage at the PCC point; in: D v This is the voltage droop factor. D q is the reactive power droop factor, and K is the reactive power-voltage loop proportional coefficient.

6. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 5, is characterized in that... The small-signal model is used to reflect the damping characteristics of the system, and its construction process is as follows: A set of nonlinear differential equations is established based on the dynamic mathematical model of each component of grid-type energy storage: Where: X is a vector composed of all state variables, and U is a vector composed of all input variables; Linearizing the above nonlinear mathematical equations at the steady-state operating point yields the small-signal model of the system: Where: A is the state matrix, and B is the input matrix; Calculate the eigenvalues ​​of state matrix A λ i Obtain the damping ratio corresponding to all eigenvalues: in: ε i The damping ratio for the i-th eigenvalue. σ i Let be the real part of the i-th eigenvalue. ω i The imaginary part of the i-th eigenvalue; The state matrix A contains information on the control parameters of the grid-type energy storage. The relative sensitivity of each control parameter to the minimum damping ratio of the system is calculated. Based on the sensitivity, the key control parameters that affect the damping characteristics are selected, thereby establishing a quantitative correspondence between the key control parameters and the system damping index.

7. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 6, is characterized in that... The coupling effect law is determined according to the following process: Based on the mapping relationship between the key control parameters and each quantitative index, each key control parameter is taken as the adjustment object, while keeping the other control parameters unchanged. The value of the control parameter is changed within a preset value range, and the corresponding quantitative indexes such as frequency change rate, frequency voltage deviation, and minimum damping ratio are calculated. The preset value range is jointly determined by the allowable range of controller parameters, the operating constraints of energy storage device, and the value range of engineering experience. Based on the direction and magnitude of the change of each quantitative index with the parameter, the individual effect and multi-characteristic synergistic / constraint relationship of the parameter on inertia, frequency, voltage support, and damping characteristics are determined, thereby clarifying the coupling effect law of each control parameter on multi-support performance indexes.

8. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 7, is characterized in that... The specific construction process of the multidimensional feasible domain of the control parameters includes: The virtual inertia coefficient is determined based on the allowable range of controller parameters, the operating constraints of the energy storage device, and the range of values ​​obtained from engineering experience. T j Damping coefficient D 1. Active power-frequency droop factor D p reactive power-voltage loop ratio D v / D q And the initial range of values ​​for the reactive power-voltage loop proportional coefficient K; For each key control parameter, set a reasonable value step size and generate a set of parameter combinations; Calculate the corresponding frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation, and minimum damping ratio; Iterate through all parameter combinations and verify whether each combination simultaneously satisfies the relevant constraints of quantitative indicators for inertia, frequency, voltage, and damping characteristics. Parameter combinations that satisfy all constraints are marked as feasible parameter combinations. The key control parameters are used to construct a control parameter space. All feasible parameters are combined in the control parameter space for set representation, resulting in a five-dimensional feasible domain of control parameters.

9. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage based on the multi-objective coordinated control of inertia, frequency, voltage, and damping as described in claim 8, is characterized in that... The aforementioned indicator weight allocation mechanism includes: The typical application scenarios for grid-type energy storage access systems are identified, including high-proportion renewable energy transmission scenarios, DC near-field scenarios, microgrids, and other scenarios that require active support from energy storage. The degree of active support demand is determined based on the current operating condition parameters, which include one or more of the following: the proportion of new energy output, system frequency deviation, PCC point voltage deviation, DC transmission power, short-circuit ratio, and oscillation mode damping ratio. The weighting coefficients of each quantitative indicator are determined based on the degree of active support demand, wherein the higher the degree of support demand, the greater the weighting coefficient of the quantitative indicator. Normalize each weight coefficient to ensure that it satisfies the non-negativity constraint and the normalization constraint.

10. The method for optimizing control parameters of high-voltage direct-connected grid-type energy storage as described in claim 9, characterized in that: The specific process of the multi-objective collaborative optimization is as follows: Using the frequency change rate, quasi-steady-state frequency deviation, PCC point voltage deviation, and minimum modal damping ratio as optimization evaluation indicators, and the multidimensional feasible region of the control parameters as the search space, a scenario-adaptive weighted comprehensive objective function is constructed by combining the weight coefficients of each indicator. The weighted comprehensive objective function takes minimizing the frequency change rate, minimizing the quasi-steady-state frequency deviation, minimizing the PCC point voltage deviation, and maximizing the minimum modal damping ratio as optimization directions. Among them, the objective of maximizing the minimum modal damping ratio is transformed into the objective of minimizing the damping performance penalty term, so that each optimization objective participates in the calculation of the comprehensive objective function. Within the multidimensional feasible region of the control parameters, the combination of key control parameters is iteratively optimized to obtain the optimal combination of grid-type energy storage control parameters that meets the support requirements of the current scenario.

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