Network construction type energy storage primary frequency modulation method and system considering SOC constraint and parameter optimization

Abstract

The invention discloses a network construction type energy storage primary frequency modulation method and system considering SOC constraint and parameter optimization. The method comprises the steps of obtaining a network construction type energy storage system frequency response transfer function; analyzing relative sensitivity of each control parameter in the network construction type energy storage system according to the frequency response transfer function of the network construction type energy storage system to obtain dominant parameters of the network construction type energy storage system participating in a primary frequency modulation process; and solving the optimization model to obtain the value of the dominant parameter, and realizing the primary frequency modulation of the network construction type energy storage system through the value of the dominant parameter. The research of the invention not only contributes to deepening the understanding of a network-building type energy storage frequency modulation mechanism, but also provides a theoretical basis and a feasible method for parameter setting and optimized operation of GFM energy storage in actual engineering, and has positive significance for promoting large-scale application of a network-building type energy storage technology in a high-proportion new energy power system.

Description

Technical Field

[0001] This invention relates to a primary frequency regulation method and system for grid-type energy storage that considers SOC constraints and parameter optimization, belonging to the field of power system control technology. Background Technology

[0002] With the transformation of the global energy structure and the continuous advancement of the "dual carbon" goal, high-proportion renewable energy sources, represented by wind power and photovoltaics, are being rapidly integrated into the power system. The replacement of traditional synchronous machines with a large number of low-inertia power electronic interface power supplies has significantly reduced the equivalent inertia level of the system. Under large disturbance conditions, this leads to problems such as large frequency drops, rapid rate of change, and oscillations during the recovery process, posing a severe challenge to the safe and stable operation of the power grid. Against this backdrop, energy storage systems, due to their characteristics of rapid response, high regulation accuracy, and bidirectional power supply, are considered an important means to improve frequency stability. Among them, grid-forming (GFM) energy storage, relying on the external characteristics of voltage sources to achieve autonomous voltage and frequency construction, possesses stable support capabilities similar to synchronous machines, and has therefore become a research focus of new power systems.

[0003] Currently, international research on grid-based energy storage mainly focuses on the basic mechanisms and control structures of grid-based converters, and the operational adaptability and dynamic performance of grid-based energy storage in weak grids, offshore wind power, and high-proportion renewable energy systems. However, in-depth modeling and systematic analysis of the collaborative operation mechanism between grid-based energy storage and synchronous generators are still lacking, especially in areas such as dynamic interaction, power angle coupling, and inertial response coordination, where a complete theoretical framework has not yet been formed. Furthermore, existing research often emphasizes performance verification under specific scenarios, while quantitative analysis and general design methods at the mechanistic level are still lacking in how to improve the overall stability and dynamic performance of the system through parameter co-optimization. In domestic research on grid-based energy storage to improve grid frequency stability, there have been in-depth analyses of the grid support capabilities of grid-based energy storage in various scenarios, including hybrid grid-based energy storage, offshore oilfield clusters, and distributed photovoltaic power in distribution networks. However, the frequency regulation performance of grid-based energy storage is not only related to the access scenario but also closely related to the configuration of its control parameters. Inappropriate parameter settings may not only lead to suboptimal frequency regulation but also cause active power oscillations, potentially triggering serious grid accidents.

[0004] In addition, existing domestic research on optimizing grid-type energy storage to improve system stability generally ignores the physical constraint of the state of charge (SOC) of energy storage batteries. It is necessary to maintain it within a safe and reasonable operating range in order to avoid overcharging and discharging, which could damage the equipment life or cause frequency regulation interruption.

[0005] Therefore, how to collaboratively optimize the control parameters of GFM energy storage under the premise of taking into account multiple practical operational constraints such as SOC, so as to fully tap its frequency regulation potential and ensure the safe and stable operation of the system, is a key scientific problem that urgently needs to be solved. Summary of the Invention

[0006] Objective: To overcome the shortcomings of existing technologies, this invention provides a grid-based energy storage primary frequency regulation method and system that considers SOC constraints and parameter optimization, in order to assess the impact of power flow imbalance and voltage instability on the power grid.

[0007] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0008] Firstly, a grid-based energy storage primary frequency regulation method considering SOC constraints and parameter optimization specifically includes:

[0009] Obtain the frequency response transfer function of the grid-type energy storage system.

[0010] Based on the frequency response transfer function analysis of the grid-type energy storage system, the relative sensitivity of each control parameter in the grid-type energy storage system is obtained, and the dominant parameters of the grid-type energy storage system participating in the primary frequency regulation process are obtained.

[0011] Solve the optimization model to obtain the values ​​of the dominant parameters, and use the values ​​of the dominant parameters to achieve primary frequency regulation of the grid-type energy storage system.

[0012] Optionally, the frequency response transfer function of the grid-type energy storage system The expression is as follows:

[0013]

[0014] in, This represents the change in power grid frequency. The active power disturbance experienced by the system. , Let these represent the equivalent inertial time constant and the equivalent damping coefficient of the system, respectively. This represents the droop coefficient of the synchronous generator. This is the frequency regulation coefficient of the speed controller. The time constant of the speed controller, For Laplace variables, , , These correspond to the port voltage on the grid-type energy storage side and the port voltage on the AC grid side, respectively. The equivalent impedance between the grid-connected energy storage and the grid connection point is... and These correspond to the active frequency regulation coefficient and virtual inertia coefficient of grid-type energy storage, respectively. This serves as the reference value for the angular frequency of grid-type energy storage. The virtual inertial time constant of the grid-type energy storage converter. This is the virtual damping coefficient of the grid-type energy storage converter.

[0015] Optionally, the step of analyzing the relative sensitivity of each control parameter in the grid-type energy storage system based on the frequency response transfer function of the grid-type energy storage system to obtain the dominant parameters for the grid-type energy storage system to participate in the primary frequency regulation process specifically includes:

[0016] The relative sensitivity of each control parameter in a grid-type energy storage system is calculated based on the frequency response transfer function of the grid-type energy storage system.

[0017] Obtain the time series of relative sensitivity of each control parameter in a grid-type energy storage system.

[0018] When the grid-type energy storage system experiences the maximum frequency deviation, the control parameter corresponding to the maximum relative sensitivity value in the time series is taken as the dominant parameter for the grid-type energy storage system to participate in the primary frequency regulation process.

[0019] Optionally, the relative sensitivity of each control parameter specifically includes:

[0020]

[0021]

[0022]

[0023]

[0024] in, The relative sensitivity representing the virtual inertial time constant. The relative sensitivity representing the virtual damping coefficient. This represents the relative sensitivity of the active frequency modulation coefficient. The relative sensitivity representing the virtual inertia coefficient. The frequency response transfer function of a grid-type energy storage system. This represents the change in power grid frequency. and These correspond to the active frequency regulation coefficient and virtual inertia coefficient of grid-type energy storage, respectively. This serves as the reference value for the angular frequency of grid-type energy storage. The virtual inertial time constant of the grid-type energy storage converter. This represents the virtual damping coefficient of a grid-type energy storage converter. , , These correspond to the port voltage on the grid-type energy storage side and the port voltage on the AC grid side, respectively. The equivalent impedance between the grid-connected energy storage and the grid connection point is... For Laplace variables, , .

[0025] Optionally, the expression for the optimization model is as follows:

[0026]

[0027] in, As the dominant parameter, This represents the minimum frequency deviation value for a grid-type energy storage system. This represents the power variation value of grid-type energy storage. The rated power of the grid-type energy storage system, For the available capacity of grid-type energy storage systems, , These are the upper and lower limits for safe operation of the SOC. The active power output of grid-type energy storage, This refers to the active power available for use by the synchronous generator set. For the frequency change rate of a grid-type energy storage system, This is the upper limit of the frequency change rate of a grid-type energy storage system.

[0028] Optionally, the algorithm for solving the optimization model is a sequential quadratic programming algorithm.

[0029] Optionally, the sequential quadratic programming algorithm specifically includes:

[0030] Step (1): Given an initial point Convergence accuracy ,make , place , Let I represent the approximate value of the initial Hessian matrix, I represent the identity matrix, and k represent the number of iterations.

[0031] Step (2): The original problem at the iteration point This can be simplified into a quadratic programming problem.

[0032] Step (3): Solve the above quadratic programming problem and let , This represents the optimal solution to a quadratic programming subproblem.

[0033] Step (4): In direction The objective function of the original problem is constrained and searched in one dimension to obtain the next iteration point. .

[0034] Step (5): Termination judgment: If If the termination criterion of a given precision is met, then... As the optimal solution. If the cost is the optimal cost of the objective function, terminate the calculation; otherwise, proceed to step (6).

[0035] Step (6): Correct according to the quasi-Newton method or the inverse rank 2 quasi-Newton method ,make Proceed to step (2).

[0036] Optional, The expression is as follows:

[0037]

[0038] in, The active power disturbance experienced by the system. As the system's baseline capacity, Represents the equivalent inertial time constant of the system. and These correspond to the changes in active power of synchronous generators and grid-type energy storage, respectively.

[0039] In a second aspect, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a primary frequency regulation method for grid-type energy storage considering SOC constraints and parameter optimization as described in any of the first aspects.

[0040] Thirdly, a computer device comprising:

[0041] Memory is used to store instructions.

[0042] A processor is configured to execute the instructions, causing the computer device to perform operations of a grid-type energy storage primary frequency regulation method considering SOC constraints and parameter optimization as described in any of the first aspects.

[0043] Beneficial Effects: This invention provides a primary frequency regulation method and system for grid-based energy storage considering SOC constraints and parameter optimization. First, a detailed mathematical model of grid-based energy storage and synchronous generators collaboratively participating in the primary frequency regulation of the system is established. Second, the closed-loop frequency response of the system is derived based on the model, and trajectory sensitivity analysis is used to identify the dominant parameters affecting transient frequency stability. Based on this, a primary frequency regulation method for grid-based energy storage considering SOC constraints and parameter optimization is proposed, aiming to achieve the optimal balance between frequency regulation performance and operational safety. Finally, the effectiveness of the proposed method is verified through simulation. This invention not only helps to deepen the understanding of the frequency regulation mechanism of grid-based energy storage but also provides a theoretical basis and feasible methods for parameter tuning and optimized operation of GFM energy storage in practical engineering, which is of positive significance for promoting the large-scale application of grid-based energy storage technology in high-proportion new energy power systems. Compared with existing technologies, the advantages of this invention are as follows:

[0044] 1. This invention identifies the dominant parameters of grid-type energy storage participating in primary frequency regulation and optimizes the configuration based on constraints to obtain specific parameters.

[0045] 2. This invention establishes a detailed mathematical model of grid-type energy storage and synchronous generators working together to participate in primary frequency regulation, and derives the system frequency response transfer function based on this model.

[0046] 3. Based on trajectory sensitivity analysis, this invention identifies the active frequency modulation coefficient as the dominant parameter affecting the transient frequency of the system.

[0047] 4. This invention constructs a parameter optimization model with the goal of minimizing the maximum frequency deviation of the system, and with constraints such as the output of grid-type energy storage, the state of charge of grid-type energy storage, the output of synchronous generator sets, and the frequency change rate of the system. The specific active frequency regulation parameters are obtained by solving the model using a sequential quadratic programming algorithm. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a primary frequency regulation method for grid-type energy storage that considers SOC constraints and parameter optimization according to the present invention.

[0049] Figure 2 This is a schematic diagram of the conventional virtual synchronous machine control strategy of the present invention.

[0050] Figure 3 This is a schematic diagram of the SFR (System Frequency Response) model of the present invention.

[0051] Figure 4 This is a schematic diagram of the topology of the grid-type energy storage system of the present invention.

[0052] Figure 5 This is a schematic diagram of the equivalent frequency response model of the grid-type energy storage system of the present invention.

[0053] Figure 6 This is a schematic diagram of the trajectory sensitivity curve of the control parameters of a grid-type energy storage system.

[0054] Figure 7 This is a schematic diagram of the least squares fitting curve.

[0055] Figure 8 This is a schematic diagram of the upper limit function for the gain coefficient.

[0056] Figure 9 The diagram shows the system frequency response for different methods.

[0057] Figure 10 This diagram illustrates the comparison of output power for different energy storage methods.

[0058] Figure 11 This diagram illustrates the comparison of SOC changes for different energy storage methods. Detailed Implementation

[0059] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0060] The present invention will be further described below with reference to specific embodiments.

[0061] Example 1:

[0062] This embodiment introduces a primary frequency regulation method for grid-based energy storage that considers SOC constraints and parameter optimization, such as... Figure 1 As shown, the specific steps include the following:

[0063] Step 1: Based on the grid-type energy storage model under the conventional virtual synchronous machine control strategy, generate the power transfer relationship of the grid-type energy storage; generate the system frequency response model of the synchronous generator based on the classic SFR (System Frequency Response) model; combine the power transfer relationship of the grid-type energy storage with the system frequency response model of the synchronous generator to generate the frequency response transfer function of the grid-type energy storage system.

[0064] Step 2: Analyze the active power frequency regulation coefficient in the grid-type energy storage system based on the frequency response transfer function. Virtual inertia coefficient Virtual inertial time constant Virtual damping coefficient For the sensitivity of frequency deviation in grid-type energy storage systems, the dominant parameters of grid-type energy storage participating in the primary frequency regulation process are identified by comparing the sensitivity of each control parameter at the moment when the system experiences the maximum frequency deviation.

[0065] Step 3: Derive the boundaries of the SOC (State of Charge), transient frequency change rate RoCoF (RFC), synchronous generator output, and grid-type energy storage output when the grid-type energy storage system participates in primary frequency regulation. Propose an optimization model with minimizing the maximum frequency deviation of the system as the optimization objective and the above boundaries as constraints. The optimal configuration of the grid-type energy storage system can be achieved by adjusting the dominant parameters in Step 2.

[0066] Furthermore, the specific steps for step 1 are as follows:

[0067] like Figure 2 As shown, based on the grid-type energy storage model under the conventional virtual synchronous machine control strategy, the active power control model of the grid-type energy storage model is obtained, and the expression of the active power control model is as follows:

[0068] (1)

[0069] In the formula: The virtual inertial time constant of the grid-type energy storage converter; For Laplace variables; This is a reference value for the angular frequency of a grid-type energy storage converter; This serves as the reference value for the angular frequency of grid-type energy storage. This is a reference value for the active power of grid-type energy storage; The virtual damping coefficient of the grid-type energy storage converter; This is a reference value for the phase angle of grid-type energy storage; This refers to the active power output of grid-type energy storage.

[0070] The rotor motion equations of the virtual synchronous generator are constructed, and the expressions of the rotor motion equations are as follows:

[0071] (2)

[0072] in, .

[0073] In the formula: This is the virtual change in work angle; This refers to the change in active power output of grid-type energy storage. For grid-type energy storage, this refers to the frequency variation. and These correspond to the active frequency regulation coefficient and virtual inertia coefficient of grid-type energy storage, respectively; The filtering time constant for grid-type energy storage; This refers to the change in the reference value of active power for grid-type energy storage.

[0074] Since the power delivered to the grid by grid-connected energy storage depends on the virtual power angle difference, the change in active power output of grid-connected energy storage can be expressed as the power transfer relationship of grid-connected energy storage as follows:

[0075] (3)

[0076] In the formula: , These correspond to the port voltage on the grid-type energy storage side and the port voltage on the AC grid side, respectively. ; This represents the change in the equivalent power angle of the power grid, and ; This refers to the change in power grid frequency. It is the equivalent impedance connected in series between the grid-type energy storage and the grid connection point.

[0077] like Figure 3 As shown, the classic SFR model is used to equivalence the synchronous generator. The classic SFR model consists of the rotor motion equation and the unit speed regulation link, which can briefly describe the dynamic coupling process between mechanical power change, load disturbance and frequency response. Considering that the internal dynamics of the turbine and valve control have limited influence on the steady-state frequency-power relationship under small disturbance conditions, and that their static gain can usually be approximated as constant, this part of the higher-order dynamics is equivalent to a first-order governor model, and the relevant influences are uniformly reflected in the frequency regulation coefficient of the governor. With time constant Therefore, the expression for the system frequency response model of the synchronous generator is as follows:

[0078] (4)

[0079] Based on the above analysis, and by integrating the grid-type energy storage model with the SFR model of synchronous generators, a model is constructed as follows: Figure 4 The diagram shows a grid-type energy storage system. For ease of analysis, line losses in the model are ignored, and a single synchronous generator is used to model an equivalent multi-synchronous generator grid. According to the rotor motion equations, the frequency deviation of the grid-type energy storage system is determined by the internal power imbalance of the system. Let the power disturbance on the load side be... This indicates the source of the external disturbance that caused the frequency change; and These correspond to the changes in active power of synchronous generator sets and grid-connected energy storage, respectively. Therefore, we can obtain the following... Figure 5 The expression for the frequency response model of the grid-type energy storage system shown is as follows:

[0080] (5)

[0081] , These represent the equivalent inertial time constant and equivalent damping coefficient of the system, respectively. This indicates the active power disturbance experienced by the system; This represents the droop coefficient of the synchronous generator.

[0082] Will and Substituting the rotor motion equations of the virtual synchronous generator (VSG), since the voltage changes little on the grid-connected energy storage side and the AC grid side, it is assumed that... , Approximately equal to 1, and due to the time constant of the grid-type energy storage filter Typically in the millisecond range, therefore, to further simplify, we ignore the smaller values ​​in the equation. Item, and thus obtain from The virtual power angle change represented by the grid-type energy storage The expression is as follows:

[0083] (6)

[0084] Will and Substituting the expression into the power transmission relationship of grid-type energy storage, and considering the small power angle difference, it is assumed that... Approximately equal to Therefore, the following power transfer relationship expression is obtained:

[0085] (7)

[0086] Will and Substituting the frequency response model of the grid-type energy storage system, the expression for the frequency response transfer function of the grid-type energy storage system is obtained as follows:

[0087] (8)

[0088] in, This is the frequency response transfer function of a grid-type energy storage system.

[0089] Furthermore, step 2 specifically includes:

[0090] To facilitate sensitivity calculations, the transfer function of the frequency response of the grid-type energy storage system can be used. After simplification, its expression is as follows:

[0091] (9)

[0092] In the formula: , , , .

[0093] The effect of system frequency deviation on the active frequency modulation coefficient is derived based on the calculation method of absolute sensitivity. Virtual inertia coefficient Virtual inertial time constant Virtual damping coefficient The partial derivatives are shown in the following equation:

[0094] (10)

[0095] (11) (12)

[0096] (13)

[0097] in, This represents the partial derivative of the system frequency deviation with respect to the virtual inertial time constant. This represents the partial derivative of the system frequency deviation with respect to the virtual damping coefficient. This represents the partial derivative of the system frequency deviation with respect to the active power frequency modulation coefficient. This represents the partial derivative of the system frequency deviation with respect to the virtual inertia coefficient.

[0098] Since the adjustable ranges of each parameter differ, their importance can be determined by comparing their relative sensitivities. The corresponding expression for relative sensitivity is as follows:

[0099] (14)

[0100] (15)

[0101] (16)

[0102] (17)

[0103] in, The relative sensitivity representing the virtual inertial time constant. The relative sensitivity representing the virtual damping coefficient. This represents the relative sensitivity of the active frequency modulation coefficient. This represents the relative sensitivity of the virtual inertia coefficient.

[0104] Substituting the relative sensitivity of the parameters in the above formula into typical values, we can plot the following: Figure 6The trajectory sensitivity curve is shown. By comparing the relative sensitivity at the moment when the grid-type energy storage system experiences the maximum frequency deviation, and taking the control parameter corresponding to the maximum relative sensitivity value as the dominant parameter, the active power frequency regulation coefficient at this time can be observed. The sensitivity to frequency response is significantly higher than that of other parameters. This result indicates that... It plays a dominant role in influencing the maximum frequency deviation. Therefore, in subsequent analysis and parameter tuning, the active power frequency regulation coefficient should be the focus and optimized. This is to further improve the system's performance in maximum frequency deviation control.

[0105] Furthermore, by analyzing the relative sensitivity of the above parameters, the dominant parameter x affecting the participation of the grid-type energy storage system in primary frequency regulation can be obtained. In order to reduce the complexity of the analysis, the frequency response transfer function expression (8) of the grid-type energy storage system is reorganized into the following expression:

[0106] (18)

[0107] in, (19)

[0108] (20)

[0109] Due to the high order of the system model, it is difficult to directly perform analytical analysis of transient characteristics using the inverse Laplace transform. To reduce the complexity of the analysis, this invention introduces an approximate linearization method for the active power frequency modulation coefficient. Verification was conducted. Based on the distribution characteristics of the simulation data, a first-order linear function can be used to approximate the relationship between the active power frequency regulation coefficient and the maximum frequency deviation of the grid-type energy storage system. Its mathematical expression is shown in equation (21):

[0110] (twenty one)

[0111] In the formula: A is the active frequency regulation coefficient. Absolute sensitivity to maximum frequency deviation; B is the equivalent constant term of the maximum frequency deviation of the system under a given disturbance when the grid-type energy storage does not participate in active frequency regulation. This indicates the maximum frequency deviation of a grid-type energy storage system.

[0112] Based on this, the expression for the relative sensitivity of the system can be further derived, as shown in equation (22):

[0113] (twenty two)

[0114] In the formula: , The initial values ​​are respectively taken for the active frequency regulation coefficient. and current value Maximum frequency deviation.

[0115] Since the relative sensitivity varies little within the studied parameter range, it can be approximated as a constant. Furthermore, the relative change relationship is converted back to its absolute form, and the simulation data is fitted using the least squares method, such as... Figure 7 As shown, the fitting curve expression of the first-order linear relationship between the maximum frequency deviation and the active frequency modulation coefficient can be obtained, as shown in equation (23):

[0116] (twenty three)

[0117] To verify the rationality of the proposed linearization method, the sensitivity values ​​of the analytical method were compared, and the relative errors of the two methods were calculated, as shown in Table 1.

[0118] Table 1 Fitting Error Analysis

[0119]

[0120] As can be seen from the comparison results in Table 1, after linearization and equivalence, the absolute sensitivity values ​​obtained by each method are basically consistent, and their relative errors are all less than 5%, which has little impact on the accuracy of optimization analysis. Therefore, the approximate linearization method can be used to characterize the active power frequency modulation coefficient. The relationship with the maximum frequency deviation is used to simplify the calculation process and improve analysis efficiency.

[0121] Furthermore, the specific operation of step 3 is as follows:

[0122] The dominant parameters of grid-based energy storage participating in primary frequency regulation are quantitatively analyzed and their boundaries are characterized. Based on the aforementioned frequency response model of the grid-based energy storage system, under the condition that the reserve capacity is unlimited, the active power that the synchronous generator can call upon is:

[0123] (twenty four)

[0124] In the formula: This refers to the rated power of the synchronous generator set; As backup power; This refers to the active power available for use by the synchronous generator set.

[0125] The maximum active power generation capacity that the system can provide after a disturbance is determined by the combined available capacity of the grid-based energy storage and the synchronous generator units, i.e., the remaining capacity of the grid-based energy storage system. Its mathematical expression is:

[0126] (25)

[0127] In the formula: This refers to the remaining capacity of a grid-type energy storage system; This refers to the rated power of a grid-type energy storage system. , These are the upper and lower limits for safe operation of the SOC. The available capacity of a grid-type energy storage system.

[0128] Based on the power balance relationship at the lowest frequency point, the constraints between the frequency support power required by the system and the remaining capacity of the system can be established, as shown in equation (26):

[0129] (26)

[0130] In the formula: The equivalent primary frequency modulation coefficient of the system and ; This refers to the frequency deviation corresponding to the primary frequency modulation dead zone. This represents the minimum frequency deviation value of a grid-type energy storage system.

[0131] To ensure that the frequency response after disturbance meets the operational safety requirements, a threshold limit needs to be applied to the deviation of the lowest frequency point, i.e., the maximum allowable frequency deviation constraint. Typically, the maximum transient frequency deviation is within 0.8 Hz, as shown in equation (27):

[0132] (27)

[0133] Combining equations (26) and (27), we can obtain:

[0134] (28)

[0135] Because an excessively large dominant parameter x may cause a sudden increase in energy storage output, exceeding the rated power or SOC constraint range of grid-type energy storage, triggering converter overcurrent protection or abnormal SOC decay, thus disrupting system stability; conversely, if the dominant parameter x is too small, the energy storage frequency regulation capability cannot be fully utilized, making it difficult to effectively compensate for system power deficits and meet frequency safety threshold requirements. Therefore, constraints are added to control the upper and lower limits of the dominant parameter x.

[0136] (27)

[0137] Substituting the frequency threshold condition of equation (27) and the equivalent expression of equation (26) into the equation, we can derive the maximum value of parameter x as:

[0138] (28)

[0139] Based on equation (28), the dominant parameter x is determined as the active power frequency regulation coefficient. For example, the functional relationship of its upper limit is as follows: Figure 8 As shown in the figure: Active frequency regulation coefficient The gain coefficient upper limit function graph is a theoretical boundary derived from the static power balance formula of the system at the lowest frequency point, neglecting the dynamic support effects such as virtual inertia and virtual damping of the grid-type energy storage system. It can be seen that the upper limit of the gain coefficient is related to the adjustable power of the grid-type energy storage and the reserve capacity of the synchronous generator set. Too large a value will lead to the loss of the limiting effect, while too small a value will result in a negative value; therefore, an appropriate value needs to be selected.

[0140] Considering the RoCoF (Rate of Change of Frequency) constraint for the power system frequency change during primary frequency regulation involving grid-connected energy storage, the frequency change rate of the grid-connected energy storage system can be obtained from the rotor motion equations:

[0141] (29)

[0142] In the formula: This represents the system's baseline capacity.

[0143] The transient frequency change rate should satisfy:

[0144] (30)

[0145] In the formula: This is the upper limit of RoCoF.

[0146] With the goal of minimizing the frequency deviation of the system after disturbance, the following optimization model is established to solve for the optimal primary frequency regulation coefficient:

[0147] (31)

[0148] In the formula, the first constraint is the output limit of the grid-type energy storage, the second constraint is the state of charge of the grid-type energy storage, the third constraint is the output limit of the synchronous generator set, and the fourth constraint is the frequency change rate constraint of the system.

[0149] The Sequential Quadratic Programming (SQP) algorithm is used to solve the model. The solution process of the Sequential Quadratic Programming algorithm mainly includes two steps: first, the original nonlinear optimization problem is transformed into a simplified quadratic optimization problem through Taylor expansion; second, the quadratic optimization problem is solved iteratively, and the iterative solution steps are as follows:

[0150] (1) Given an initial point Convergence accuracy ,make , place ;

[0151] (2) The original problem at the iteration point This can be simplified into a quadratic programming problem.

[0152] (3) Solve the above quadratic programming problem and let ;

[0153] (4) In direction The objective function of the original problem is constrained and searched in one dimension to obtain the next iteration point. ;

[0154] (5) Termination judgment: If If the termination criterion of a given precision is met, then... As the optimal solution. If the cost is the optimal cost of the objective function, terminate the calculation; otherwise, go to (6).

[0155] (6) Corrected according to DFP or BFGS method ,make , turn (2).

[0156] This represents an approximation of the initial Hessian matrix. In the SQP algorithm, the Hessian matrix (or its approximation) is used to describe the curvature information of the objective function, thus guiding the search direction. This is the initial estimate of the Hessian matrix at the start of the iterative process. Typically, without better prior information, This will be set to the identity matrix I. I represents the identity matrix, which is a diagonal matrix where all diagonal elements are 1s and all other elements are 0s. In the SQP algorithm, this will... Initializing to an identity matrix implies assuming the objective function has uniform curvature in the initial iterations. k represents the iteration number; the SQP algorithm is an iterative process that gradually approaches the optimal solution through multiple iterations. k is used to record the current iteration number, and its value increases by 1 after each iteration. The optimal solution to the quadratic programming subproblem is represented by the SQP algorithm, which approximates the original nonlinear programming problem as a quadratic programming (SQP) subproblem in each iteration. It is the search direction obtained after solving the SQP subproblem, which is simply the direction from the current iteration point to the next iteration point.

[0157] Finally, the model was optimized using a quadratic sequential programming algorithm implemented in simulation software to solve for the minimum value of a constrained nonlinear multivariate function, thus obtaining the optimal dominant parameter x value of the grid-type energy storage system.

[0158] Example 2:

[0159] This embodiment describes a computer-readable storage medium storing a computer program that, when executed by a processor, implements a primary frequency regulation method for grid-type energy storage considering SOC constraints and parameter optimization as described in any of Embodiment 1.

[0160] Example 3:

[0161] This embodiment describes a computer device, including:

[0162] Memory is used to store instructions.

[0163] A processor is configured to execute the instructions, causing the computer device to perform an operation of a grid-type energy storage primary frequency regulation method considering SOC constraints and parameter optimization as described in any of Embodiment 1.

[0164] Example 4:

[0165] This embodiment verifies the effectiveness of the method proposed in this invention during a single frequency modulation process by establishing a simulation system in the simulation software. Figure 3 The simulation system shown is compared and analyzed with three schemes: "no energy storage", "conventional parameters of grid-type energy storage" and "the method proposed in this invention".

[0166] The simulation system parameters are shown in Table 2 and have been converted to per-unit values. The total system load is 1.0 pu, and the input load disturbance is 0.1 pu. Furthermore, in the dynamic simulation of this example, because the virtual inertia and damping effectively suppress frequency drops in the early stages of the disturbance, the actual stability margin of the system is much higher than the estimate from the static formula. Therefore, considering the adjustable power of the grid-type energy storage is 0.2 pu and the reserve capacity of the conventional units is 0.15 pu, the active power frequency regulation coefficient is... The upper limit is 37.5 and the lower limit is 0.

[0167] Table 2 Simulation System Parameters

[0168]

[0169] By solving equation (31) using an optimization scheme, the optimal active frequency regulation coefficient can be obtained. It is 35.8.

[0170] Figure 9The system frequency response processes under the same load disturbance conditions were compared among three schemes: "no energy storage," "grid-based energy storage with conventional parameters," and "the method proposed in this paper." It can be seen that without energy storage participating in primary frequency regulation, the system frequency drop amplitude is the largest, the lowest frequency point is low, and the system frequency safety margin is insufficient. However, the system frequency response performance is improved to some extent after introducing grid-based energy storage, and the lowest frequency point is raised, indicating that energy storage participation in primary frequency regulation can enhance the system's frequency support capability. Furthermore, compared with the conventional parameter scheme, the lowest frequency point is further improved and the maximum frequency deviation is significantly reduced under the proposed method. Table 3 provides a quantitative comparison of key frequency indicators under the three schemes. The results show that, without changing the system disturbance conditions, the proposed method can more effectively suppress frequency drops while satisfying system constraints. This result demonstrates the effectiveness of the optimized active power frequency regulation coefficient. It better balances frequency support capability and power constraints, resulting in a comprehensive improvement in system transient performance.

[0171] Table 3 compares key indicators of system frequency response.

[0172]

[0173] Figure 10 The table shows the active power output response of grid-based energy storage under different schemes. It can be seen that when using conventional parameter schemes, grid-based energy storage can participate in the primary frequency regulation process of the system and provide some active power support, but its output level is relatively limited. In contrast, the grid-based energy storage under the proposed method can more fully release its frequency regulation potential in the early stages of disturbances, providing greater active power support, and achieving more full utilization of the frequency regulation capability of grid-based energy storage without exceeding the rated frequency regulation capacity constraint. As shown in Table 4:

[0174] Table 4 compares key indicators of energy storage output power.

[0175]

[0176] Figure 11 The variation of SOC in grid-based energy storage under three schemes is illustrated. It can be seen that under the conventional parameter scheme, the SOC change after disturbance is relatively small, and energy consumption is relatively limited. In the method proposed in this paper, because grid-based energy storage undertakes more primary frequency regulation support tasks, its SOC decrease is relatively larger. However, while improving the system's frequency response performance, it also takes into account the energy constraint characteristics of the energy storage system, achieving a balance between frequency support capability and energy storage operation safety. As shown in Table 5:

[0177] Table 5 Comparison of Key Energy Storage SOC Indicators

[0178]

[0179] Table 5 compares the changes in SOC of grid-based energy storage under two schemes: "conventional parameters for grid-based energy storage" and "the method proposed in this paper." Specific data shows that when the initial SOC is 60%, the proposed method results in a more significant change in SOC during the primary frequency regulation process. However, after the disturbance ends (t=15s), the SOC under the conventional parameter scheme drops to 54.39%, the total consumption SOC is 5.61%, and the maximum discharge rate is 0.73% / s. In contrast, the proposed method reduces the SOC to 51.75%, increases the total consumption SOC to 8.25%, and raises the maximum discharge rate to 1.06% / s. This demonstrates that the proposed method, by optimizing the active power frequency regulation coefficient, enables the energy storage to release more energy during frequency regulation, thus more effectively supporting the system frequency. Simultaneously, it achieves a balance between frequency regulation performance and energy storage operational safety within the permissible SOC safety range.

[0180] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0184] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A primary frequency regulation method for grid-type energy storage considering SOC constraints and parameter optimization, characterized in that: Specifically, it includes: Obtain the frequency response transfer function of a grid-type energy storage system; Based on the frequency response transfer function analysis of the grid-type energy storage system, the relative sensitivity of each control parameter in the grid-type energy storage system is obtained, and the dominant parameters of the grid-type energy storage system participating in the primary frequency regulation process are obtained. Solve the optimization model to obtain the values ​​of the dominant parameters, and use the values ​​of the dominant parameters to achieve primary frequency regulation of the grid-type energy storage system.

2. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The frequency response transfer function of the grid-type energy storage system The expression is as follows: ； in, This represents the change in power grid frequency. The active power disturbance experienced by the system. , Let these represent the equivalent inertial time constant and the equivalent damping coefficient of the system, respectively. This represents the droop coefficient of the synchronous generator. This is the frequency regulation coefficient of the speed controller. The time constant of the speed controller, For Laplace variables, , , These correspond to the port voltage on the grid-type energy storage side and the port voltage on the AC grid side, respectively. The equivalent impedance between the grid-connected energy storage and the grid connection point is... and These correspond to the active frequency regulation coefficient and virtual inertia coefficient of grid-type energy storage, respectively. This serves as the reference value for the angular frequency of grid-type energy storage. The virtual inertial time constant of the grid-type energy storage converter. This is the virtual damping coefficient of the grid-type energy storage converter.

3. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The process of analyzing the relative sensitivity of various control parameters in a grid-type energy storage system based on its frequency response transfer function yields the dominant parameters for grid-type energy storage participation in primary frequency regulation, specifically including: The relative sensitivity of each control parameter in a grid-type energy storage system is calculated based on the frequency response transfer function of the grid-type energy storage system. Obtain the time series of relative sensitivity of each control parameter in a grid-type energy storage system; When the grid-type energy storage system experiences the maximum frequency deviation, the control parameter corresponding to the maximum relative sensitivity value in the time series is taken as the dominant parameter for the grid-type energy storage system to participate in the primary frequency regulation process.

4. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The relative sensitivity of each control parameter specifically includes: ； ； ； ； in, The relative sensitivity representing the virtual inertial time constant. The relative sensitivity representing the virtual damping coefficient. This represents the relative sensitivity of the active frequency modulation coefficient. The relative sensitivity representing the virtual inertia coefficient. The frequency response transfer function of a grid-type energy storage system. This represents the change in power grid frequency. and These correspond to the active frequency regulation coefficient and virtual inertia coefficient of grid-type energy storage, respectively. This serves as the reference value for the angular frequency of grid-type energy storage. The virtual inertial time constant of the grid-type energy storage converter. This represents the virtual damping coefficient of a grid-type energy storage converter. , , These correspond to the port voltage on the grid-type energy storage side and the port voltage on the AC grid side, respectively. The equivalent impedance between the grid-connected energy storage and the grid connection point is... For Laplace variables, , .

5. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The expression for the optimization model is as follows: ； in, As the dominant parameter, This represents the minimum frequency deviation value for a grid-type energy storage system. This represents the power variation value of grid-type energy storage. The rated power of the grid-type energy storage system, For the available capacity of grid-type energy storage systems, , These are the upper and lower limits for safe operation of the SOC. The active power output of grid-type energy storage, This refers to the active power available for use by the synchronous generator set. For the frequency change rate of a grid-type energy storage system, This is the upper limit of the frequency change rate of a grid-type energy storage system.

6. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The algorithm for solving the optimization model is a sequential quadratic programming algorithm.

7. The method for primary frequency regulation of grid-type energy storage considering SOC constraints and parameter optimization according to claim 1, characterized in that: The sequential quadratic programming algorithm specifically includes: Step (1): Given an initial point Convergence accuracy ,make , place , This represents an approximation of the initial Hessian matrix, where I represents the identity matrix and k represents the number of iterations. Step (2): The original problem at the iteration point This can be simplified into a quadratic programming problem. Step (3): Solve the above quadratic programming problem and let , Represents the optimal solution to a quadratic programming subproblem; Step (4): In direction The objective function of the original problem is constrained and searched in one dimension to obtain the next iteration point. ; Step (5): Termination judgment: If If the termination criterion of a given precision is met, then... As the optimal solution. If the cost is the optimal cost of the objective function, terminate the calculation; otherwise, proceed to step (6). Step (6): Correct according to the quasi-Newton method or the inverse rank 2 quasi-Newton method ,make Proceed to step (2).

8. A primary frequency regulation method for grid-type energy storage considering SOC constraints and parameter optimization according to claim 5, characterized in that: The expression is as follows: ； in, The active power disturbance experienced by the system. As the system's baseline capacity, Represents the equivalent inertial time constant of the system. and These correspond to the changes in active power of synchronous generators and grid-type energy storage, respectively.

9. A computer-readable storage medium, characterized in that: It stores a computer program that, when executed by a processor, implements a primary frequency regulation method for grid-type energy storage that considers SOC constraints and parameter optimization as described in any one of claims 1 to 8.

10. A computer device, characterized in that: include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the computer device to perform the operation of a primary frequency regulation method for grid-type energy storage considering SOC constraints and parameter optimization as described in any one of claims 1 to 8.

Images