Complex microgrid system and method for constructing frequency response model thereof

Abstract

The embodiment of the application provides a complex micro-grid system and a construction method of a frequency response model thereof, and belongs to the technical field of complex micro-grid. The construction method comprises obtaining the values of an equivalent active frequency droop coefficient, an equivalent inertia time constant and an equivalent output power characteristic of the complex micro-grid system. The construction method of the complex micro-grid system and the frequency response model thereof provided by the application obtains the values of the equivalent active frequency droop coefficient, the equivalent inertia time constant and the equivalent output power characteristic, obtains a unified transfer model of the complex micro-grid system according to the values, and finally calculates the frequency response model of the complex micro-grid system. The frequency response model can be applied to different control modes of different micro-grid systems, has wide universality, and solves the problems that the complex micro-grid system is difficult to model and is difficult to realize in the prior art.

Description

Technical Field

[0001] This invention relates to the field of complex microgrid technology, and more specifically to a method for constructing a complex microgrid system and its frequency response model. Background Technology

[0002] To maintain frequency stability in a microgrid system, a three-layer control structure is implemented from top to bottom. These control layers have different control objectives, response times, and communication requirements. The first layer, the microsource control layer, establishes the system's voltage and frequency in islanded mode; VSG control and droop control are two commonly used control strategies. The second layer, the microgrid control layer, primarily coordinates control at the microgrid system level, maintaining system frequency stability through secondary frequency regulation. The third layer, the energy management layer, prioritizes economic efficiency and safety. Based on load forecasting, distributed generation forecasting, and operating conditions, it calculates the planned power of each distributed generation (DG) unit and formulates load adjustment schemes, which are then sent to the secondary control layer.

[0003] In modern control theory, modeling the system is necessary before designing a control system. The model of the controlled system is both the starting point and the goal of the control system design. Therefore, modeling the microgrid system is also required before designing a secondary frequency control system for a microgrid. Currently, mechanistic modeling and identification modeling are commonly used. Mechanistic modeling involves identifying the internal mechanisms of the controlled system to establish a dynamic model. The advantage of this method is that the model is accurate and has clear physical meaning. However, with the continuous development of microgrids, their internal structures are becoming increasingly complex, and the types of energy storage converters are also increasing. Moreover, most energy storage converters are commercial products, making it difficult to obtain detailed parameter information. Even if parameter information is obtained, the large number of parameters and the complex and variable structure of the microgrid make mechanistic modeling extremely difficult, and the resulting model may be unusable due to its complex structure and high order. Identification modeling, on the other hand, involves pre-designing the structure of the controlled system model and then using identification algorithms to process the input and output data of the system to obtain the coefficients in the model structure. Compared with mechanism modeling, this method is simpler and easier to implement, and does not require knowledge of the detailed internal structure and parameter information of the controlled system. However, this method only determines the system model based on the degree of fitting of the output waveform. The model can only reflect the frequency output, but the physical meaning of the model parameters is unclear and cannot effectively reflect the mechanism of the system, which is not conducive to applying the model to more scenarios.

[0004] In the process of realizing this invention, the inventors of this application discovered that the above-mentioned solutions of the prior art have the defects of low universality, difficulty in modeling and implementation of complex microgrid systems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing a complex microgrid system and its frequency response model. This method has a unified transfer model for various control methods of complex microgrids, thereby improving the versatility of the complex microgrid system model and reducing the modeling difficulty.

[0006] To achieve the above objectives, one embodiment of the present invention provides a method for constructing a frequency response model of a complex microgrid, comprising:

[0007] To obtain the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system;

[0008] The unified transmission model of the complex microgrid system is calculated according to formula (1).

[0009] (1)

[0010] in, To output the change in angular velocity, The change in output power The equivalent active frequency droop coefficient is given. The equivalent inertial time constant is mentioned above;

[0011] The frequency response model of the complex microgrid system is calculated according to formula (2).

[0012] (2)

[0013] in, For output frequency, The equivalent output power characteristic is... The sampling period.

[0014] Optionally, obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system includes:

[0015] The equivalent active frequency droop coefficient is calculated according to formula (3).

[0016] (3)

[0017] in, For the first in the complex microgrid system The active frequency droop factor of an inverter. The number of inverters in the complex microgrid system is an integer.

[0018] Optionally, obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic of a complex microgrid system also includes:

[0019] The equivalent inertial time constant is calculated according to formula (4).

[0020] (4)

[0021] in, For the first The inertial time constant of an inverter.

[0022] Optionally, obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic of a complex microgrid system also includes:

[0023] The equivalent output power characteristic is calculated according to formula (5).

[0024] (5)

[0025] in, For the first The output power characteristics of the inverter.

[0026] Optionally, obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic of a complex microgrid system also includes:

[0027] Randomly obtain initial values ​​and encode them to obtain the initial population;

[0028] Calculate the fitness function value of the population;

[0029] Determine whether the fitness function value is less than or equal to a preset value;

[0030] If the fitness function value of the population is greater than a preset value, a new generation of individuals with the same number as the population is selected based on the fitness function value of each individual in the population.

[0031] The selected new generation individuals are subjected to replication, crossover, and mutation operations to form a new generation population;

[0032] Then return to the step of calculating the fitness function value of the population.

[0033] Optionally, obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic of a complex microgrid system also includes:

[0034] If the fitness function value of the population is less than or equal to a preset value, the minimum value of the intermediate power value of a single individual in the population is selected.

[0035] The optimal solutions for the equivalent active frequency droop coefficient, the equivalent inertia time constant, and the equivalent output power characteristics are identified based on the minimum value.

[0036] Optionally, calculating the fitness function value of the population includes:

[0037] The intermediate power value is calculated according to formulas (6) and (7).

[0038] (6)

[0039] (7)

[0040] in, The sampling step size, The first in the population The power value of each individual, Numbered by integer. The first in the population Reference power for each individual;

[0041] The fitness function value of the population is calculated according to formula (8).

[0042] (8)

[0043] in, The fitness function value is... The power value obtained from sampling. The total number of individuals in the population.

[0044] Optionally, the construction method further includes:

[0045] Determine the control mode of the complex microgrid system;

[0046] When determining that the control mode of the complex microgrid system is droop control, the transfer model under droop control is calculated using formula (9).

[0047] (9)

[0048] in, The droop coefficient for active power-frequency under droop control;

[0049] When determining that the control mode of a complex microgrid system is VSG control, the transfer model under VSG control is calculated using formula (10).

[0050] (10)

[0051] in, For rotational inertia, The damping coefficient is... This refers to the active power-frequency regulation coefficient under VSG control mode. This is the rated angular velocity.

[0052] On the other hand, the present invention also provides a system for a complex microgrid, comprising:

[0053] Unschedulable micro-sources;

[0054] Multiple inverters, the multiple inverters are connected in parallel, and one end of the multiple inverters is connected to the unschedulable micro source;

[0055] An energy storage device, connected to the other end of the plurality of inverters, is used to store the electrical energy of the unschedulable micro-source;

[0056] Multiple loads are connected to the other end of the multiple inverters;

[0057] A controllable switch, one end of which is connected to the other end of the plurality of inverters, and the other end is used to connect to the power grid;

[0058] A controller is used to execute any of the construction methods described above.

[0059] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the construction methods described above.

[0060] Through the above technical solution, the present invention provides a method for constructing a complex microgrid system and its frequency response model. This method obtains the values ​​of the equivalent active power frequency droop coefficient, the equivalent inertial time constant, and the equivalent output power characteristic, and uses these values ​​to obtain a unified transfer model for the complex microgrid system. Finally, it calculates the frequency response model of the complex microgrid system. This frequency response model is applicable to different control methods in different microgrid systems, exhibiting wide versatility and solving the problem of difficult and impractical modeling of complex microgrid systems in existing technologies.

[0061] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0062] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0063] Figure 1This is a flowchart of a method for constructing a frequency response model of a complex microgrid system according to an embodiment of the present invention;

[0064] Figure 2 This is a flowchart illustrating the method for obtaining equivalent output power characteristics in the construction of a frequency response model for a complex microgrid system according to an embodiment of the present invention.

[0065] Figure 3 This is a flowchart of the genetic algorithm in a method for constructing a frequency response model of a complex microgrid system according to an embodiment of the present invention;

[0066] Figure 4 This is a flowchart illustrating the process of obtaining fitness values ​​in a method for constructing a frequency response model of a complex microgrid system according to an embodiment of the present invention.

[0067] Figure 5 This is a flowchart illustrating the control mode determination process in a method for constructing a frequency response model of a complex microgrid system according to an embodiment of the present invention.

[0068] Figure 6 This is an example diagram of genetic algorithm identification in the frequency response model of a complex microgrid system according to an embodiment of the present invention;

[0069] Figure 7 This is an example diagram of frequency response waveform fitting in a frequency response model of a complex microgrid system according to an embodiment of the present invention. Detailed Implementation

[0070] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0071] Figure 1 This is a flowchart illustrating a method for constructing a frequency response model of a complex microgrid according to an embodiment of the present invention. Figure 1 In this context, the construction method may include:

[0072] In step S10, the values ​​of the equivalent active power droop coefficient, equivalent inertia time constant, and equivalent output power characteristic of the complex microgrid system are obtained. In a complex microgrid system, if the parameters of each inverter are known, these parameters can be calculated numerically, but the calculation process is complex and computationally intensive. In actual modeling, it is not necessary to calculate the exact values ​​of these parameters; approximate values ​​can be obtained through circuit analysis. Furthermore, if the parameters of each inverter in the complex microgrid system are unknown, the values ​​of these parameters can be obtained using a genetic algorithm.

[0073] In step S11, the unified transfer model of the complex microgrid system is calculated according to formula (1).

[0074] (1)

[0075] in, To output the change in angular velocity, The change in output power This is the equivalent active frequency droop factor. It is the equivalent inertial time constant.

[0076] In step S12, the frequency response model of the complex microgrid system is calculated according to formula (2).

[0077] (2)

[0078] in, For output frequency, For equivalent output power characteristics, Take 20ms.

[0079] In steps S10 to S12, it is necessary to first obtain the equivalent active power frequency droop system, equivalent inertial time constant, and equivalent output power characteristics of the complex microgrid system. Then, the values ​​of the equivalent active power frequency droop system, equivalent inertial time constant, and equivalent output power characteristics are input into the unified transfer model to obtain the unified transfer model of the complex microgrid system. Based on the unified transfer model, the frequency response model of the complex microgrid system is finally obtained.

[0080] Traditional microgrid system modeling typically employs two methods: mechanistic modeling and identification modeling. In complex microgrids, the variety of energy storage converter types and the difficulty in obtaining their parameters make mechanistic modeling extremely challenging, and the resulting models are often unusable due to their complex structure and high order. Furthermore, identification modeling relies solely on the fit of the output waveform to determine the system model, resulting in poor versatility and limiting its application to various scenarios. In this embodiment of the invention, by inputting the values ​​of the equivalent active power frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics into a constructed unified transfer model, a unified frequency response model for complex microgrid systems can be obtained. This unified frequency response model is applicable to different control methods in complex microgrid systems, possesses strong versatility, and can be applied to various scenarios. It facilitates the design of microgrid-related controller parameters and also solves the problems of difficult and impractical modeling of complex microgrid systems.

[0081] In this embodiment of the invention, in order to obtain the equivalent active power frequency droop coefficient, the equivalent inertia time constant, and the equivalent output power characteristics, it is also necessary to analyze and calculate the complex microgrid system. Specifically, the construction method may further include, for example: Figure 2 The steps are shown. In Figure 2 In addition, the construction method may also include:

[0082] In step S20, the equivalent active frequency droop coefficient is calculated according to formula (3).

[0083] (3)

[0084] in, For the first in complex microgrid systems The active frequency droop factor of an inverter. This represents the number of inverters in the complex microgrid system, and it is an integer.

[0085] In step S21, the equivalent inertial time constant is calculated according to formula (4).

[0086] (4)

[0087] in, For the first The inertial time constant of an inverter.

[0088] In step S22, the equivalent output power characteristic is calculated according to formula (5).

[0089] (5)

[0090] in, For the first The output power characteristics of the inverter.

[0091] In steps S20 to S22, the equivalent power frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic are calculated sequentially according to formulas (3) to (5). Considering that the modeling process does not require precise values ​​of the power frequency droop coefficient, inertia time constant, and output power characteristic, equivalent approximate values ​​of the power frequency droop coefficient, inertia time constant, and output power characteristic can be obtained from the circuit. The method of obtaining the values ​​of the equivalent power frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic through circuit analysis is accurate and effective, and the acquisition method is simple, reducing the difficulty of modeling complex microgrid systems.

[0092] In this embodiment of the invention, to obtain the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics, a genetic algorithm can also be used to obtain the optimal solutions for the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics. Specifically, the genetic algorithm may include, for example: Figure 3 The steps are shown. Specifically, in Figure 3 In this context, the genetic algorithm may include:

[0093] In step S30, initial values ​​are randomly obtained and encoded to obtain initial populations. Specifically, initial values ​​for the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic need to be randomly obtained, and these three initial values ​​are encoded to form initial populations for the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic, respectively. The encoding method can be any form known to those skilled in the art, such as binary encoding, Gray code encoding, etc.

[0094] In step S31, the fitness function value of the population is calculated. Specifically, after obtaining the initial population values ​​for the equivalent active frequency droop coefficient, the equivalent inertial time constant, and the equivalent output power characteristic, respectively, the fitness function values ​​for these three populations need to be calculated. The fitness function value indicates whether the population of that generation is suitable as the optimal solution.

[0095] In step S32, it is determined whether the fitness function value is less than or equal to a preset value. Specifically, to determine the optimal solutions for the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics, the calculated fitness function values ​​are compared with their corresponding preset values ​​to determine whether the current generation of the population can be output as the optimal solution.

[0096] In step S33, if the fitness function value of the population is greater than a preset value, a new generation of individuals is selected based on the fitness function value of each individual in the population, with the same number of individuals as the population. Specifically, if the fitness function value of the equivalent power frequency droop coefficient, the equivalent inertial time constant, or the equivalent output power characteristic is greater than the corresponding preset value, it indicates that this generation of the population does not meet the requirements for outputting the optimal solution. In this case, each individual in the new generation needs to be selected optimally, with the number of selected individuals matching the number of individuals in previous generations, to ensure that each generation can continue to calculate and that each generation increasingly approaches the optimal solution. The selection method for each generation includes, but is not limited to, roulette wheel selection, where the selection probability is determined based on the median power value of each individual in the new generation.

[0097] In step S34, the selected new generation individuals undergo replication, crossover, and mutation operations to form a new generation population. Specifically, the selected individuals are subjected to replication, crossover, and mutation operations according to preset probabilities or randomly, resulting in the same number of new individuals. Finally, a new generation population with equivalent active frequency droop coefficient, equivalent inertial time constant, and equivalent output power characteristics is formed.

[0098] In step S35, the process returns to the step of calculating the fitness function value of the population. After obtaining the new generation of the population with equivalent active frequency droop coefficient, equivalent inertial time constant, and equivalent output power characteristics, it is necessary to further determine whether this new generation of the population can be output as the optimal solution. Therefore, the fitness function value of this new generation of the population needs to be calculated here.

[0099] In step S36, if the fitness function value of the population is less than or equal to a preset value, the minimum intermediate power value of a single individual in the population is selected. Specifically, if the fitness function value of the equivalent active frequency droop coefficient, the fitness function value of the equivalent inertial time constant, or the fitness function value of the equivalent output power characteristic is less than or equal to the corresponding preset value, it indicates that this generation of the population can be used as the optimal solution output. At the same time, in order to improve the applicability and accuracy of the optimal solution, the individual with the smallest intermediate power value in this generation of the population is selected to facilitate the subsequent construction of the optimal transfer model.

[0100] In step S37, the optimal solutions for the corresponding equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristic are identified based on the minimum value. Specifically, the individual with the smallest fitness function value in this generation needs to be translated into a specific numerical value, which is the optimal solution for the equivalent active frequency droop coefficient, equivalent inertia time constant, or equivalent output power characteristic.

[0101] In steps S30 to S37, the initial values ​​of the equivalent active frequency droop coefficient, equivalent inertial time constant, or equivalent output power characteristic are first encoded to form a corresponding initial population. Then, the fitness function value of this initial population is calculated and compared with a preset value. If the fitness function value is greater than the preset value, it indicates that the current generation of the population does not meet the output conditions of the optimal solution, and further replication, crossover, and mutation operations are required to form a new generation of population, and this process is repeated. If the fitness function value is less than or equal to the preset value, it indicates that the current generation of the population meets the output conditions of the optimal solution. The individual with the smallest intermediate power value in this generation is selected for translation and output, thereby obtaining the optimal solution for the equivalent active frequency droop coefficient, equivalent inertial time constant, or equivalent output power characteristic. This genetic algorithm can obtain the optimal solutions for the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of all inverters in a microgrid system without knowing the inverter parameters in the microgrid system. This enables the unified model and frequency response model to be constructed in different scenarios.

[0102] In this embodiment of the invention, in order to calculate the fitness function value of the population, it is also necessary to calculate the intermediate power value of each individual in the population. Specifically, the genetic algorithm may include, for example: Figure 4 The steps are shown. Specifically, in Figure 4 In this context, the genetic algorithm may also include:

[0103] In step S40, the intermediate power value is calculated according to formulas (6) and (7).

[0104] (6)

[0105] (7)

[0106] in, Let the sampling step size be denoted as . , The first in the population The power value of each individual, Numbered by integer. The first in the population Reference power for each individual.

[0107] The transfer function of the complex microgrid system is obtained from the equivalent control block diagram of the traditional equivalent model, as shown in equation (18).

[0108] (18)

[0109] in, This represents the total output power of the inverter.

[0110] Expressing equation (18) in the discrete domain yields the differential equation shown in equation (19).

[0111] (19)

[0112] By processing formula (19) using the three-point interpolation derivative formula, we can obtain the recursive formulas shown in formulas (6) and (7).

[0113] In step S41, the fitness function value of the population is calculated according to formula (8).

[0114] (8)

[0115] in, The fitness function value is... The power value obtained from sampling. The total number of individuals in the population.

[0116] In steps S40 to S41, the intermediate power value of the current generation population is first calculated, and then the fitness function value of the current generation population is calculated based on the intermediate power value. Then, the fitness function value can be used to determine whether the current generation population can be output as the optimal solution, so as to realize the effective construction of a unified transmission model for complex microgrid systems.

[0117] In this embodiment of the invention, in order to clarify the frequency response model of the complex microgrid system, it is also necessary to determine the specific control method of the complex microgrid system. Specifically, the construction method may include, for example: Figure 5 The steps are shown. Specifically, in Figure 5 In addition, the construction method may also include:

[0118] In step S50, it is determined whether the control mode of the complex microgrid system is droop control. The control mode for unschedulable micro-sources such as photovoltaic and wind power in the microgrid system is the same as when connected to the main grid. Whether in grid-connected or islanded mode, they must operate in PQ control mode, running according to Maximum Power Point Tracking (MPPT) or power-limited operation according to the microgrid central controller's scheduling. Schedulable micro-sources in the microgrid system require different control modes in grid-connected and islanded operation modes. When the microgrid system is grid-connected, the schedulable micro-sources need to input or absorb a specified amount of power from the grid according to the requirements of the microgrid central controller. The inverter needs to be controlled in current control mode, which can be PQ control, droop control, or virtual synchronous machine control. When the microgrid system operates in islanded mode, the schedulable micro-sources need to have voltage source characteristics to establish the voltage and frequency of the microgrid. In this case, the inverter of the micro-source needs to operate in voltage control mode, generally VF control, droop control, or virtual synchronous machine control. In summary, inverters in complex microgrid systems currently employ four control methods under both grid-connected and islanded operation modes: PQ control, VF control, droop control, and virtual synchronous machine control.

[0119] The purpose of PQ control is to enable the active and reactive power outputs of micro-sources to track their reference signals in real time. Each micro-source does not participate in frequency and voltage regulation; these are provided by the main power grid. Therefore, it has little impact on frequency response modeling, and PQ-controlled micro-sources can be disregarded.

[0120] Constant voltage and constant frequency (VF) control means controlling the output voltage frequency of a microsource to remain constant. In a master-slave microgrid, under islanded operation, a dispatchable microsource typically needs to operate in VF control mode to establish the microgrid's voltage frequency and handle varying load power. A VF-controlled power supply can be considered equivalent to a voltage source, exhibiting low transmission impedance and is unsuitable for parallel operation. Therefore, this control method is generally not used in centralized microgrid peer-to-peer control structures.

[0121] Therefore, the control methods for complex microgrid systems are generally droop control and virtual synchronous machine control.

[0122] In step S51, when it is determined that the control mode of the complex microgrid system is droop control mode, the transfer model under droop control mode is calculated using formula (9).

[0123] (9)

[0124] in, The droop coefficient is the active power-frequency ratio under droop control.

[0125] Droop control utilizes the primary regulation principle of power systems, specifically the droop characteristic of synchronous generators. The converter's output frequency and power exhibit a droop relationship. The inverter's output power is achieved through a filter impedance and then through the line impedance. The active and reactive power outputs of the inverter are calculated using formulas (11) and (12).

[0126] (11)

[0127] (12)

[0128] in, It is the active power output by the inverter. It is the reactive power output of the inverter. This is the voltage at the inverter port. The output impedance is the output impedance on the inverter output side. This is the filter impedance on the output side of the inverter. The voltage on the grid side. This is the phase angle difference. When the output impedance is purely inductive, that is, the resistors in formulas (11) and (12) Furthermore, the power angle in the system is generally small (in this case, it approximately satisfies...) ， Then the active power can be seen. Mainly related to the angle of attack Related to It is irrelevant, and Mainly with Related to, and the angle of the action It is irrelevant. Therefore, the relationship between the inverter's active power-frequency and reactive power-voltage can be derived, as shown in formulas (13) and (14).

[0129] (13)

[0130] (14)

[0131] in, The reactive power-voltage droop coefficient under droop control. This serves as a reference value for the active power transmitted to the power grid or load. This serves as a reference value for reactive power transmitted to the power grid or load. Rated power, Rated voltage, This refers to the inverter output frequency. This is the inverter output voltage.

[0132] Then, calculate the active power-frequency droop coefficient under droop control in the complex microgrid system according to formula (15).

[0133] (15)

[0134] in, The output frequency change is given. Finally, formula (9) is obtained according to formula (15).

[0135] In step S52, when determining that the control mode of the complex microgrid system is VSG control mode, the transfer model under VSG control mode is calculated using formula (10).

[0136] (10)

[0137] in, For rotational inertia, The damping coefficient is... The active power-frequency regulation coefficient under VSG control mode. Virtual synchronous generator (VSG) control introduces a synchronous generator model into the inverter control system, allowing the static power electronic converter to operate like a rotating motor, with the output frequency changing with the power. Based on the characteristics of synchronous generators in the power grid, the main idea of ​​VSG is based on the mechanical oscillation equation. The rotor motion equation is obtained according to formula (16).

[0138] (16)

[0139] in, Moment of inertia is used to describe the magnitude of the inverter's rotational inertia. It represents the damping coefficient, which describes the time it takes for an inverter to recover to a balanced state when faced with a disturbance; Electromagnetic power represents the power consumed by the inverter to suppress back EMF. This represents mechanical power, used to describe how fast the inverter performs its work. ω is the angular velocity.

[0140] According to formula (17), the mechanical motion equation is obtained through the speed regulator.

[0141] (17)

[0142] in, The reference power is given by the VSG control mode. Formula (10) is obtained from formulas (16) and (17).

[0143] In steps S50 to S52, there are two control methods for complex microgrid systems: droop control and virtual synchronous machine control. Both control methods have a unified transfer model. Based on the unified transfer model, a unified frequency response model for complex microgrid systems can be obtained. This model is applicable to different control methods for different microgrid systems, has strong versatility, and is widely used. It solves the problem of difficult and hard-to-implement modeling of complex microgrid systems in the prior art.

[0144] On the other hand, the present invention also provides a complex microgrid system, which may include an unschedulable micro-source, multiple inverters, energy storage devices, multiple loads, controllable switches, and a controller.

[0145] Multiple inverters are connected in parallel, with one end of each inverter connected to an unschedulable micro-source. An energy storage device is connected to the other end of the inverters to store electrical energy from the unschedulable micro-source. Multiple loads are connected to the other end of the inverters, and one end of a controllable switch is connected to the other end of the inverters, with the other end of the controllable switch used for grid connection. A controller is used to execute any of the above construction methods. Unschedulable micro-sources include photovoltaic and wind power, while the energy storage device is generally a dispatchable micro-source.

[0146] In another aspect, the present invention also provides a computer-readable storage medium that can store instructions for being read by a machine to cause the machine to perform any of the above-described construction methods.

[0147] In this embodiment of the present invention, in order to verify the recognition capability of the genetic algorithm of the present invention, the following simulation scheme was designed:

[0148] The simulation parameters for the inverter controlled by four converters are set as follows: ; ; , , =40; , , =40. Substituting into formulas (9) and (10), the actual equivalent active frequency droop coefficient is calculated. Equivalent inertial time constant In the genetic algorithm, the population size is set to 100, the stopping generation is 50, and the fitness function value deviation is... The simulation results are as follows: Figure 6 As shown, the calculated optimal fitness value and average fitness value tend to 0, indicating that the difference between the actual output and the simulated output value is getting smaller and smaller. Finally, the optimal individual that satisfies the optimization termination condition is output, which is the identification value.

[0149] Substitute the identified equivalent coefficient values ​​into formula (2), apply a power disturbance to the system, observe the waveform of the output frequency change, and fit the output frequency of the actual model with the output frequency of the simulation model. The results are as follows: Figure 7 As shown, the goodness of fit reached 96.47%, indicating that the equivalent model can effectively reflect the system situation and is beneficial to the optimization of controller parameters.

[0150] Through the above technical solution, the present invention provides a method for constructing a complex microgrid system and its frequency response model. This method obtains the values ​​of the equivalent active power frequency droop coefficient, the equivalent inertial time constant, and the equivalent output power characteristic, and uses these values ​​to obtain a unified transfer model for the complex microgrid system. Finally, it calculates the frequency response model of the complex microgrid system. This frequency response model is applicable to different control methods in different microgrid systems, exhibiting strong versatility and solving the problem of difficult and impractical modeling of complex microgrid systems in existing technologies.

[0151] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] 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.

[0154] 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.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0157] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0158] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0159] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for constructing a frequency response model of a complex microgrid system, characterized in that, include: To obtain the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system; The unified transmission model of the complex microgrid system is calculated according to formula (1). ，（1） in, To output the change in angular velocity, The change in output power The equivalent active frequency droop coefficient is given. The equivalent inertial time constant is mentioned above; The frequency response model of the complex microgrid system is calculated according to formula (2). ，（2） in, For output frequency, The equivalent output power characteristic is... The sampling period.

2. The construction method according to claim 1, characterized in that, The values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system include: The equivalent active frequency droop coefficient is calculated according to formula (3). ，（3） in, For the first in the complex microgrid system The active frequency droop factor of an inverter. The number of inverters in the complex microgrid system is an integer.

3. The construction method according to claim 2, characterized in that, Obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system also includes: The equivalent inertial time constant is calculated according to formula (4). ，（4） in, For the first The inertial time constant of an inverter.

4. The construction method according to claim 3, characterized in that, Obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system also includes: The equivalent output power characteristic is calculated according to formula (5). ，（5） in, For the first The output power characteristics of the inverter.

5. The construction method according to claim 1, characterized in that, The values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system include: Randomly obtain initial values ​​and encode them to obtain the initial population; Calculate the fitness function value of the population; Determine whether the fitness function value is less than or equal to a preset value; If the fitness function value of the population is greater than a preset value, a new generation of individuals with the same number as the population is selected based on the fitness function value of each individual in the population. The selected new generation individuals are subjected to replication, crossover, and mutation operations to form a new generation population; Then return to the step of calculating the fitness function value of the population.

6. The construction method according to claim 5, characterized in that, Obtaining the values ​​of the equivalent active frequency droop coefficient, equivalent inertia time constant, and equivalent output power characteristics of a complex microgrid system also includes: If the fitness function value of the population is less than or equal to a preset value, the minimum value of the intermediate power value of a single individual in the population is selected. The optimal solutions for the equivalent active frequency droop coefficient, the equivalent inertia time constant, and the equivalent output power characteristics are identified based on the minimum value.

7. The construction method according to claim 6, characterized in that, Calculating the fitness function value of the population includes: ，（6） ，（7） in, The sampling step size, The first in the population The power value of each individual Numbered by integer. The first in the population Reference power for each individual; The fitness function value of the population is calculated according to formula (8). ，（8） in, The fitness function value is... The power value obtained from sampling. The total number of individuals in the population.

8. The construction method according to claim 1, characterized in that, The construction method also includes: Determine the control mode of the complex microgrid system; When determining that the control mode of the complex microgrid system is droop control, the transfer model under droop control is calculated using formula (9). ，（9） in, The droop coefficient for active power-frequency under droop control; When determining that the control mode of a complex microgrid system is VSG control, the transfer model under VSG control is calculated using formula (10). ，（10） in, For rotational inertia, The damping coefficient is... This refers to the active power-frequency regulation coefficient under VSG control mode. This is the rated angular velocity.

9. A complex microgrid system, characterized in that, include: Unschedulable micro-sources; Multiple inverters, the multiple inverters are connected in parallel, and one end of the multiple inverters is connected to the unschedulable micro source; An energy storage device, connected to the other end of the plurality of inverters, is used to store the electrical energy of the unschedulable micro-source; Multiple loads are connected to the other end of the multiple inverters; A controllable switch, one end of which is connected to the other end of the plurality of inverters, and the other end is used to connect to the power grid; A controller for performing the construction method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that are read by a machine to cause the machine to perform the construction method as described in any one of claims 1 to 8.

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