Frequency response analysis method, system and device based on multi-type resource inertia response and medium

Abstract

The invention discloses a frequency response analysis method, system and device based on multi-type resource inertia response and a medium, and belongs to the technical field of power system operation and control, and the method comprises the steps: building virtual inertia control strategies for a photovoltaic system, an energy storage system and an electric vehicle charging station, and obtaining the inertia response coefficients of various resources; a feedback adjustment link is introduced into the classical frequency response model, a feedback coefficient is set based on an inertia response coefficient, and a frequency response model containing multiple types of resources is constructed; and establishing and solving a frequency response dynamic equation to obtain a relational expression between the frequency deviation and the power disturbance. According to the method, the frequency response model considering multi-type resource cooperative adjustment is established, so that quantitative representation of inertia response characteristics of different types of resources is realized, a parameter framework that a traditional model only considers inertia of a synchronous machine is broken through, and a theoretical basis is provided for accurately predicting frequency dynamic characteristics of a low-inertia system.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a frequency response analysis method, system, equipment, and medium based on the inertia response of multiple types of resources. Background Technology

[0002] Driven by the global energy transition and the "dual carbon" goal, new power systems are rapidly evolving towards a high proportion of renewable energy and high proportion of power electronic equipment integration. Among these, distributed resources such as photovoltaic power plants, electrochemical energy storage systems, and electric vehicle charging stations have achieved large-scale grid-connected operation due to their clean, low-carbon, and flexible characteristics. However, this transformation also presents disruptive challenges to the frequency stability control system of traditional power systems.

[0003] The limitations of classical frequency response models are becoming increasingly apparent. These models primarily characterize the frequency dynamics dominated by synchronous machines, considering only core parameters such as synchronous machine inertia and governor time constants. They cannot effectively incorporate the inertia response mechanisms of power electronic interface resources such as photovoltaics, energy storage, and electric vehicles, nor can they accurately describe the interaction of multiple types of resources participating in frequency regulation. This leads to a significant decrease in the model's prediction accuracy for the frequency dynamics of low-inertia systems, failing to provide reliable theoretical support for subsequent control strategy design. Therefore, how to construct a frequency response model that accurately reflects the inertia response characteristics of multiple types of resources such as photovoltaics, energy storage, and charging, and design efficient collaborative control strategies based on this model to improve the frequency stability of low-inertia systems and suppress frequency deviations and fluctuations under power disturbances, has become a critical issue that urgently needs to be addressed in the field of new power system operation and control.

[0004] To address the aforementioned technical challenges, this invention, based on a thorough analysis of the inertia response mechanisms of photovoltaic, energy storage, and electric vehicle charging stations, breaks through the parameter framework of the classic frequency response model. It innovatively introduces photovoltaic output adjustment coefficients, energy storage SOC adjustment coefficients, and electric vehicle cluster adjustment coefficients. By establishing multi-parameter coupled dynamic equations, a frequency dynamic model considering the inertia response of multiple resource types is constructed, laying a core theoretical foundation for the subsequent optimized design of frequency control strategies for low-inertia systems. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a frequency response analysis method, system, device and medium based on the inertia response of multiple types of resources.

[0006] Therefore, the technical problem solved by this invention is that with the large-scale grid connection of power electronic interface resources such as photovoltaics, energy storage, and electric vehicles, the proportion of traditional synchronous generators has continued to decline, leading to a significant reduction in system inertia. Furthermore, the response speed and regulation capabilities of different types of distributed resources vary significantly, resulting in diversified and discrete characteristics in system inertia support. Classical frequency response models only characterize the frequency dynamic process dominated by synchronous machines and cannot effectively incorporate the inertia response mechanisms of multiple resource types. They are also unable to accurately describe the interaction effects of these resources when collaboratively participating in frequency regulation, leading to insufficient prediction accuracy for the frequency dynamic characteristics of low-inertia systems.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a frequency response analysis method based on the inertia response of multiple types of resources, comprising, Virtual inertia control strategies were established for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively, and the inertia response coefficients of various resources were obtained. A feedback adjustment loop is introduced into the classic frequency response model. The feedback adjustment loop sets the feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. Establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and power disturbance, and calculate the system frequency deviation under power disturbance based on the relationship expression.

[0008] As a preferred embodiment of the frequency response analysis method based on the inertia response of multiple resource types described in this invention, the step of establishing virtual inertia control strategies for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively, and obtaining the inertia response coefficients of various resources, includes: For photovoltaic systems, a virtual inertia control strategy is established through the load reduction control of power electronic devices, and the inertia response coefficient of the photovoltaic system is obtained by utilizing the energy regulation characteristics of energy storage elements when the frequency changes. For energy storage systems, a virtual inertia control strategy is established based on a virtual synchronous generator model, and the inertia response coefficient of the energy storage system is obtained through the frequency dynamic response characteristics. For electric vehicle charging stations, a virtual inertia control strategy is established based on the power regulation capability of electric vehicle clusters, and the inertia response coefficient of the electric vehicle charging station is obtained through adjustable power calculation.

[0009] As a preferred embodiment of the frequency response analysis method based on the inertia response of multiple types of resources described in this invention, the method involves introducing a feedback adjustment loop into the classical frequency response model. The feedback adjustment loop sets feedback coefficients based on the inertia response coefficients, and includes: The inertial response coefficients are converted into feedback coefficients using a parameter conversion method. The feedback coefficient is introduced into the feedback adjustment branch of the corresponding resource in the classical frequency response model; Set an inertial response dead zone so that the feedback adjustment circuit does not operate when the frequency fluctuation is lower than the preset dead zone threshold.

[0010] The beneficial effects of this preferred technical solution are as follows: by establishing the conversion relationship between the inertial response coefficient and the feedback coefficient through parameter conversion, different types of resources can be connected to the classical frequency response model in a unified form; by introducing the feedback coefficient into the feedback adjustment branch of the corresponding resource, the coordinated frequency adjustment of multiple types of resources is realized; by setting the inertial response dead zone mechanism, unnecessary actions of the feedback adjustment link are avoided when the frequency fluctuation is small, the frequent adjustment of the control system is reduced, and the stability and economy of system operation are improved.

[0011] As a preferred embodiment of the frequency response analysis method based on the inertia response of multiple types of resources described in this invention, the method for photovoltaic systems involves establishing a virtual inertia control strategy through load shedding control of power electronic devices, and obtaining the inertia response coefficient of the photovoltaic system by utilizing the energy regulation characteristics of energy storage elements during frequency changes. This includes: The photovoltaic operating voltage is controlled at a position higher than the optimal control voltage by load reduction control; The DC capacitor after the converter releases or stores energy through voltage fluctuations when the system frequency changes. Establish the correlation between the change in DC capacitor energy and the rate of frequency change, and determine the inertia response coefficient of the photovoltaic system based on the correlation.

[0012] As a preferred embodiment of the frequency response analysis method based on the inertia response of multiple types of resources described in this invention, wherein: the conversion of the inertia response coefficients into feedback coefficients through parameter transformation includes: The photovoltaic feedback coefficient is obtained by multiplying the inertial response coefficient of the photovoltaic system by the preset conversion coefficient. The energy storage feedback coefficient is obtained by multiplying the inertial response coefficient of the energy storage system by the preset conversion coefficient. The electric vehicle feedback coefficient is obtained by multiplying the inertial response coefficient of the electric vehicle charging station by the preset conversion coefficient.

[0013] The beneficial effects of this preferred technical solution are as follows: by multiplying the inertial response coefficients of various resources by preset conversion coefficients, parameter mapping from the inertial domain to the feedback control domain is realized, so that the inertial response characteristics of photovoltaic systems, energy storage systems and electric vehicle charging stations can be accurately converted into feedback parameters in the frequency response model; this conversion method ensures that different types of resources have consistent physical meaning and comparability in the model, and improves the model's accuracy in characterizing the frequency regulation process of multiple types of resources working together.

[0014] As a preferred embodiment of the frequency response analysis method based on multi-type resource inertia response described in this invention, the step of solving the frequency response dynamic equation to obtain the relationship expression between system frequency deviation and power disturbance includes: The frequency response dynamic equation is transformed from the time domain to the frequency domain through mathematical transformation, resulting in a frequency domain expression for the frequency deviation. The power disturbance is expressed as a step function and substituted into the frequency domain expression; The frequency domain expression is converted into a time domain expression by inverse transformation, thus obtaining the mathematical relationship between frequency deviation and time.

[0015] As a preferred embodiment of the frequency response analysis method based on multi-type resource inertia response described in this invention, wherein: the step of calculating the frequency deviation of the system under power disturbance according to the relational expression includes: Differentiate the time-domain expression for the frequency deviation; The extreme values ​​of the frequency deviation and the corresponding time points are calculated using the derivative results. The frequency stability of the system is evaluated based on the extreme values ​​and time points.

[0016] This invention provides a frequency response analysis system based on the inertia response of multiple types of resources.

[0017] To solve the above technical problems, the present invention provides the following technical solution: a frequency response analysis system based on the inertia response of multiple types of resources, comprising: a virtual inertia control module, used to establish virtual inertia control strategies for photovoltaic systems, energy storage systems and electric vehicle charging stations respectively, and to obtain the inertia response coefficients of various types of resources; A frequency response model construction module is used to introduce a feedback adjustment link into a classic frequency response model. The feedback adjustment link sets a feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. The frequency deviation calculation module is used to establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and the power disturbance, and calculate the system frequency deviation under the power disturbance based on the relationship expression.

[0018] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the frequency response analysis method based on the inertia response of multiple types of resources.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the frequency response analysis method based on the inertia response of multiple types of resources.

[0020] The beneficial effects of this invention are as follows: By establishing virtual inertia control strategies for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively and obtaining the inertia response coefficients of various resources, a quantitative characterization of the inertia response characteristics of different types of resources is achieved; by introducing a feedback adjustment loop based on the inertia response coefficient into the classical frequency response model, the traditional model breaks through the parameter framework that only considers the inertia of the synchronous machine and establishes a frequency response model that takes into account the coordinated adjustment of multiple types of resources; by solving the frequency response dynamic equation, the relationship expression between frequency deviation and power disturbance is obtained, providing a theoretical basis for accurately predicting the frequency dynamic characteristics of low-inertia systems under power disturbance, and laying the foundation for the subsequent optimization design of frequency control strategies. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a structural diagram of a frequency response model based on the inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0023] Figure 2 This is a classical frequency response model structure diagram of a frequency response analysis method based on the inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0024] Figure 3 This is a simplified frequency response model structure diagram of a frequency response analysis method based on the inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0025] Figure 4 This is a simulation topology diagram of a frequency response analysis method based on the inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0026] Figure 5 The frequency response curve of a 10MW power disturbance is provided as an embodiment of the present invention, based on a frequency response analysis method for inertia response of multiple types of resources.

[0027] Figure 6This invention provides a 20MW power disturbance curve based on a frequency response analysis method for inertia response of multiple types of resources, as an embodiment of the present invention.

[0028] Figure 7 The frequency response curve of a 30MW power disturbance is provided as an embodiment of the present invention, based on a frequency response analysis method for inertia response of multiple types of resources.

[0029] Figure 8 The figure shows the fitting results of a 10MW power disturbance based on a frequency response analysis method for inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0030] Figure 9 The figure shows the fitting results of a 20MW power disturbance based on a frequency response analysis method for inertia response of multiple types of resources, provided as an embodiment of the present invention.

[0031] Figure 10 The figure shows the fitting results of a 30MW power output based on a frequency response analysis method for inertia response of multiple types of resources, provided as an embodiment of the present invention. Detailed Implementation

[0032] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0033] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a frequency response analysis method based on the inertia response of multiple types of resources, including: Step 1: Establish virtual inertia control strategies for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively, and obtain the inertia response coefficients of various resources; Step 2: Introduce a feedback adjustment loop into the classic frequency response model. The feedback adjustment loop sets the feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. Step 3: Establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and power disturbance, and calculate the system frequency deviation under power disturbance based on the relationship expression.

[0034] Traditional power system frequency control is centered on synchronous generators, whose rotor inertia provides natural inertial support during power disturbances. Frequency stability is maintained through the coordinated action of speed governors and excitation systems, forming a mature and reliable control logic. However, with the widespread adoption of resources such as photovoltaics, energy storage, and electric vehicles that rely on power electronic converters, the proportion of synchronous generators in the system has been declining, leading to a significant reduction in overall inertia levels. Simultaneously, the response speeds and regulation capabilities of different types of distributed resources vary significantly, resulting in diversified and discrete characteristics of system inertia support. This makes the frequency dynamics more complex, and traditional control systems are no longer sufficient to cope with the frequency fluctuation risks under power disturbances.

[0035] Furthermore, this embodiment starts with the classical frequency response model and introduces the frequency response components of three types of controllable load resources—distributed photovoltaic, energy storage, and electric vehicle charging stations—to construct a frequency response model that includes multiple types of loads. Finally, based on the frequency fluctuation range required for the safe and stable operation of the power system as stipulated by national standards, the total frequency regulation capacity required by all node loads is obtained by solving the model and using the active power regulation capability of the distribution network load in the form of inertia response coefficients.

[0036] Example 2, an embodiment of the present invention, provides a frequency response analysis method based on the inertia response of multiple resource types, based on the previous embodiment, including: Step 1: Establishing virtual inertia control strategies for photovoltaic systems, energy storage systems, and electric vehicle charging stations, and obtaining the inertia response coefficients of various resources, includes the following steps A1-A3: A1: For photovoltaic systems, a virtual inertia control strategy is established through the load reduction control of power electronic devices, and the inertia response coefficient of the photovoltaic system is obtained by utilizing the energy regulation characteristics of energy storage elements when the frequency changes. A2: For energy storage systems, a virtual inertia control strategy is established based on a virtual synchronous generator model, and the inertia response coefficient of the energy storage system is obtained through the frequency dynamic response characteristics. A3: For electric vehicle charging stations, a virtual inertia control strategy is established based on the power regulation capability of electric vehicle clusters, and the inertia response coefficient of the electric vehicle charging station is obtained through adjustable power calculation.

[0037] It should be noted that, for photovoltaic systems, the virtual inertia control strategy established through load shedding control of power electronic devices, and the inertia response coefficient of the photovoltaic system obtained by utilizing the energy regulation characteristics of energy storage elements during frequency changes, includes: The photovoltaic operating voltage is controlled at a position higher than the optimal control voltage by load reduction control; The DC capacitor after the converter releases or stores energy through voltage fluctuations when the system frequency changes. Establish the correlation between the change in DC capacitor energy and the rate of frequency change, and determine the inertia response coefficient of the photovoltaic system based on the correlation.

[0038] In this embodiment, step 1 establishes a virtual inertia control strategy by: for photovoltaic systems, controlling the photovoltaic operating voltage to be higher than the optimal control voltage through load reduction control, utilizing the voltage fluctuations of the DC capacitor after the Boost converter to release or store energy when the system frequency changes, establishing the correlation between capacitor energy storage change and frequency change rate, and obtaining the photovoltaic inertia response coefficient; for energy storage systems, introducing rotor motion equations based on a virtual synchronous generator model, determining the timing of inertia support withdrawal by real-time monitoring of frequency change rate, stopping inertia response when the frequency change rate is greater than or equal to zero, and obtaining the energy storage inertia response coefficient; for electric vehicle charging stations, calculating the minimum state of charge that the electric vehicle needs to reach during the inertia support period based on the user's expected travel time and desired state of charge, determining the adjustable power by comparing the current battery state of charge with the minimum state of charge, and thus obtaining the electric vehicle charging station's inertia response coefficient.

[0039] In an optional implementation, in step 1, the virtual inertia control strategy can be established by: adopting a droop control strategy and calculating the power adjustment of various resources based on the product of the system frequency deviation and the preset droop coefficient.

[0040] In another alternative implementation, in step 1, the virtual inertia control strategy can also be established by: adopting a fixed power response strategy, whereby various resources output a pre-set fixed adjustment power when the system frequency deviation exceeds a preset threshold.

[0041] In this embodiment of the application, in step A1, the energy storage element includes: a DC capacitor on the high-voltage side after the Boost converter. The energy storage capacity of the capacitor is positively correlated with the square of the initial voltage and the capacitance. When the system frequency changes, it releases or stores electrical energy instantaneously through voltage fluctuations. When the capacitor voltage changes, it changes the active power output of the photovoltaic unit by changing the energy stored in the capacitor.

[0042] In an optional implementation, in step A1, the energy storage element may include a supercapacitor with high power density and fast charge / discharge characteristics, which can quickly adjust the energy storage state by monitoring changes in system frequency to achieve the inertial response of the photovoltaic system.

[0043] In another optional implementation, in step A1, the energy storage element may further include: a flywheel energy storage device, which stores and releases energy through the rotational kinetic energy of the rotor, responds to system frequency fluctuations by utilizing changes in rotational speed, and provides mechanical inertia support for the photovoltaic system.

[0044] Step 2: Introducing a feedback adjustment mechanism into the classic frequency response model, wherein the feedback adjustment mechanism sets the feedback coefficient based on the inertia response coefficient, and constructing a frequency response model that includes multiple types of resources, including the following steps B1-B3: B1: Convert the inertial response coefficients into feedback coefficients using a parameter conversion method; B2: Introduce the feedback coefficient into the feedback adjustment branch of the corresponding resource in the classical frequency response model; B3: Set the inertia response dead zone. When the frequency fluctuation is lower than the preset dead zone threshold, the feedback adjustment loop will not operate.

[0045] In this embodiment of the application, in step 2, the feedback adjustment link is achieved by multiplying the inertial response coefficient of the photovoltaic system, the inertial response coefficient of the energy storage system, and the inertial response coefficient of the electric vehicle charging station by the conversion coefficient to obtain the corresponding feedback coefficients. Each feedback coefficient is then introduced into the feedback adjustment branch of the corresponding resource in the classical frequency response model. At the same time, an inertial response dead zone is set. When the frequency fluctuation is less than the dead zone threshold, the feedback adjustment link does not operate, thus avoiding frequent adjustments of load power by the power electronic control equipment due to minor system disturbances.

[0046] In an optional implementation, in step 2, the feedback adjustment can be achieved by directly introducing the inertia response coefficients of various resources as feedback coefficients into the corresponding branches of the classical frequency response model, thus simplifying the parameter setting process.

[0047] In another optional implementation, in step 2, the feedback adjustment can also be achieved by setting a segmented feedback coefficient according to the magnitude of the frequency deviation, using a smaller feedback coefficient when the frequency deviation is small and a larger feedback coefficient when the frequency deviation is large.

[0048] In this embodiment of the application, in step B1, the parameter conversion method is as follows: the photovoltaic system's inertial response coefficient is multiplied by the conversion coefficient 4π to obtain the photovoltaic feedback coefficient; the energy storage system's inertial response coefficient is multiplied by the conversion coefficient 4π to obtain the energy storage feedback coefficient; and the electric vehicle charging station's inertial response coefficient is multiplied by the conversion coefficient 4π to obtain the electric vehicle feedback coefficient. This conversion coefficient takes into account the relationship between frequency and angular frequency.

[0049] In an optional implementation, in step B1, the parameter conversion method can be: establishing a correspondence table between inertia response coefficients and feedback coefficients, and determining the corresponding feedback coefficients by looking up the table based on the numerical range of the inertia response coefficients of various resources.

[0050] In another optional implementation, in step B1, the parameter conversion method can also be achieved by: setting the minimum and maximum values ​​of the inertia response coefficient corresponding to the feedback coefficient boundary values, and using linear interpolation to calculate the corresponding feedback coefficient based on the position of the inertia response coefficient of various resources within the interval.

[0051] It should be noted that the conversion of the inertial response coefficient into feedback coefficients via parameter transformation includes: The photovoltaic feedback coefficient is obtained by multiplying the inertial response coefficient of the photovoltaic system by the preset conversion coefficient. The energy storage feedback coefficient is obtained by multiplying the inertial response coefficient of the energy storage system by the preset conversion coefficient. The electric vehicle feedback coefficient is obtained by multiplying the inertial response coefficient of the electric vehicle charging station by the preset conversion coefficient.

[0052] Step 3: Establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and the power disturbance, and calculate the system frequency deviation under power disturbance based on the relationship expression, including the following steps C1-C6: C1: The frequency response dynamic equation is transformed from the time domain to the frequency domain through mathematical transformation to obtain the frequency domain expression of the frequency deviation; C2: Express the power disturbance as a step function and substitute it into the frequency domain expression; C3: Convert the frequency domain expression into a time domain expression through inverse transformation to obtain the mathematical relationship between frequency deviation and time.

[0053] C4: Perform derivative operation on the time-domain expression of the frequency deviation; C5: Calculate the extreme values ​​of the frequency deviation and the corresponding time points using the derivative results; C6: Evaluate the frequency stability of the system based on the extreme values ​​and time points mentioned above.

[0054] Example 3, referring to Figures 1-3 As an embodiment of the present invention, based on the previous embodiment, a frequency response analysis method based on the inertia response of multiple types of resources is provided, including: In step 1, to enable the photovoltaic system to have inertia support capability, when a power deficit occurs in the power system, the grid-connected photovoltaic units based on power electronic devices can adjust the load shedding control to keep the photovoltaic operating voltage above the optimal control voltage. This allows the photovoltaic system to reduce its output power through load shedding control, thereby providing inertia support when the system frequency rises and suppressing the rapid rise in system frequency. Currently, the photovoltaic operating voltage can be controlled above the optimal control voltage; the corresponding load shedding mode voltage is... Those who have contributed their efforts Photovoltaic power generation load shedding rate under load shedding control It can be represented as: (1) Among them, P m This represents the power at the maximum power tracking point.

[0055] When load shedding is implemented, the photovoltaic system possesses inertia-supported power, with the DC capacitor element after the Boost converter in the photovoltaic power generation device participating in the inertia response. This capacitor can instantaneously release or store electrical energy through voltage fluctuations, and its energy storage capacity is positively correlated with the square of the initial voltage and the capacitor's capacitance. When the system frequency rises sharply, the capacitor absorbs excess energy, raising the voltage and limiting the photovoltaic output. Assume the initial voltage across the DC capacitor on the high-voltage side after the Boost converter is... At this time, the capacitor Stored energy It can be represented as: (2) When the system frequency changes due to power fluctuations, additional control methods are used to change the voltage of the capacitors accordingly. By altering the energy stored in the capacitors, the active power output of the photovoltaic unit is changed. At that time, the capacitor releases energy. for: (3) Therefore, by changing the voltage across the capacitor to alter its stored energy, a photovoltaic (PV) unit can be made to possess inertial response capability, thus yielding the PV inertial response coefficient. The inertial response capability of photovoltaics is .

[0056] Furthermore, the establishment of a virtual inertia control strategy for the energy storage system includes the following steps: Virtual inertial control, in the VSG (Virtual Synchronous Generator) model, simulates the inertial response characteristics of a synchronous machine by introducing rotor motion equations. Specifically, the VSG achieves active-frequency control through the following equations: (4) In the formula, This represents the change in active power that the energy storage system dynamically adjusts in response to deviations in the rate of frequency change. The inertial response coefficient of the energy storage is used to simulate the inertial response of the synchronous generator.

[0057] Based on the characteristics of new energy support, in virtual inertia control, the frequency differential signal at the grid connection point of new energy power stations is monitored in real time. Determine the timing for inertial support withdrawal. This allows the inertial response of new energy sources to exit the system promptly, preventing further increase in the frequency regulation pressure on the synchronous generator units. The virtual inertia control strategy for energy storage can be defined as follows: (5) The above strategy enables the energy storage system to reproduce the dynamic external characteristics of a synchronous generator through the coordination of inertial links, thereby providing effective inertial support for low-inertia power systems.

[0058] Furthermore, the establishment of a virtual inertia control strategy for electric vehicle charging stations includes the following steps: Electric vehicles need to meet users' electricity demands, so the focus is on analyzing their technically adjustable capacity. In this regard, this invention argues that when the system encounters disturbances, electric vehicle charging stations only have charging functions and can only reduce load power, meaning they can only reduce the power consumed by the charging station, which is equivalent to outputting power to the grid.

[0059] Based on user electricity demand, the expected travel time of the nth electric vehicle is provided. and the expected state of charge at this moment. Set the current inertia support time as Therefore, the originally expected value can be calculated according to equation (6). Minimum state of charge that an electric vehicle needs to achieve at any given time .

[0060] (6) In the formula: The rated charging power for the nth electric vehicle; and These represent the charging efficiency and rated capacity of the nth electric vehicle, respectively.

[0061] For scenarios requiring a reduction in load power, if the battery charge is always greater than [a certain value]... If the user's electricity demand can still be met, the charging power can be reduced to zero within the current control cycle; otherwise, the electric vehicle needs to be charged at a certain power. At this time, the adjustable power It can be represented as: (7) In the formula: Indicates the first time during the inertia support period Adjustable power of an electric vehicle.

[0062] By leveraging virtual inertia support based on electric vehicle clusters, the operational stability of power grids with high renewable energy penetration rates can be significantly improved in low-inertia scenarios. To simplify the electric vehicle frequency response model, a virtual inertia response strategy based on a simplified VSG model is adopted.

[0063] (8) in, and Represents electric vehicle clusters The Middle The inertial response coefficient and inertial response power of an electric vehicle. This represents the rate of change of the system frequency.

[0064] Power system frequency containing electric vehicle clusters The dynamic change process can be expressed by the oscillation equation (9): (9) Among them, H G This represents the generator's inertia.

[0065] Substituting equation (9) into equation (8), we obtain the virtual inertia that the electric vehicle charging station cluster can provide to the system as follows: (10) In step 2, the classical frequency response model assumes that the system is dominated by three parameters throughout the entire frequency response process, namely the time constant of the reheater. Inertial time constant and governor droop control coefficient Among them, the reheat time constant The time constant is on the order of 6 to 12 seconds, dominating the response of the turbine's power output; inertial time constant The duration is on the order of 3 to 6 seconds, and it is always amplified by a 2x gain, thus amplifying its importance; droop control coefficient It acts as a gain in the frequency response process by dominating through its reciprocal form. Further assumptions are made regarding the reheat time constant. With inertial time constant The frequency response dominated for the first few seconds, resulting in a simplified frequency response model, which is the classical frequency response model. Its control block diagram is shown below. Figure 2 As shown, Figure 2 In the model, the parameters are as follows: This represents the increased power setpoint input to the power control system, expressed in per-unit value. This represents the mechanical power of the steam turbine, expressed as a per-unit value. This represents the power output of the external power grid, expressed in per-unit values. This represents the acceleration power of the synchronous motor, expressed as a per-unit value. Represents angular acceleration, per unit value; This indicates the proportion of power generated by the steam turbine to the total power; Represents the reheat time constant, in seconds; Represents the inertial time constant of a synchronous motor, in seconds; This represents the damping constant of the synchronous motor; The gain factor represents the input mechanical power; This represents the droop control coefficient for synchronous motors.

[0066] This invention is based on a classic frequency response model that is simple, low-order but includes basic system dynamics and can be used to estimate the frequency behavior of large power systems. It introduces a feedback regulation link that includes distributed photovoltaic, energy storage and electric vehicle charging stations. At the same time, it simplifies some parameters and control links in the frequency response model, constructs a frequency response model based on the inertia response of multiple types of resources, and provides a mathematical expression for the total frequency response coefficient required by the system from the solution of the model.

[0067] for Figure 2 The classic frequency response model shown first examines the input variables, which are the increased power point input to the power control system. With external power grid load power For this invention, the multi-type load participation in the power grid inertia response process only focuses on the power disturbance of the power grid, that is, the load power of the external power grid. Since the control process does not involve changes to the power control setpoint, this invention simplifies the above model as follows, considering that during the research process... The simplified frequency response model control block diagram is as follows: Figure 3 As shown, Figure 3 middle, Let be the disturbance power in the system, when When this occurs, it is considered that the load power in the system has suddenly decreased, such as when a tie line in the power system trips; when At this time, it is considered that the power generation capacity of the system suddenly decreases, such as when the power feeder line in the power system trips.

[0068] exist Figure 3 Based on the simplified model, feedback control links for distributed photovoltaics, energy storage, and electric vehicle charging stations are further introduced, thus completing the frequency response model based on the inertia response of multiple resource types, such as... Figure 1 As shown, Figure 1 In China, the newly introduced , , These represent the sum of the inertial response coefficients of photovoltaic, energy storage, and electric vehicle charging stations participating in system inertial regulation, respectively, and are the model input values. The power measured is taken from the synchronous generator bus, regional tie line, and DC feeder, which may cause frequency fluctuations during the operation of the power system. For the identification of other parameters in the model, the frequency response curve can be fitted by applying multiple sets of power perturbations to the model.

[0069] Meanwhile, in order to avoid the power electronic control equipment in the system from frequently adjusting the load power due to minor disturbances in the system, according to the national standards for photovoltaics and energy storage, as well as the requirements of the National Power System Safety and Stability Control Technology Guidelines, the model sets an inertial response dead zone of 0.03Hz for photovoltaics, energy storage, and electric vehicle charging stations. When the frequency fluctuation is less than 0.03Hz, the load does not respond to the inertial response fluctuation of the system.

[0070] Furthermore, in step 3, after the system encounters a disturbance, the synchronous generator's response power changes. Considering the load response, the generator's inertial response, and primary frequency regulation, the dynamic equation for the frequency response of the grid-connected system containing new energy sources can be expressed as: (11) (12) in, Indicates system inertia. Indicates generator capacity. Indicates photovoltaic capacity, Indicates energy storage capacity. Indicates the capacity of electric vehicles. Indicates the total system capacity. Indicates the system disturbance power. This represents the synchronous generator response power. Indicates load response power. Indicates the damping coefficient Based on the above system frequency response model containing multiple types of load resources, the frequency domain expression of the frequency deviation during the system frequency response process can be derived mathematically as follows: (13) in: (14) Therefore, based on arbitrary power perturbations The angular velocity or frequency of the system can be calculated. Furthermore, since the power disturbance studied in this embodiment is a sudden disturbance, It takes the form of a step function, that is: (15) in Indicates the magnitude of the disturbance power. The unit step signal, converted to the frequency domain, is: (16) Substituting this expression into (16), the result is: (17) Transforming the frequency domain expression into the time domain, we obtain the time domain expression for the system frequency deviation as follows: (18) in: (19) Taking the derivative of the time-domain expression, we obtain the following expression for the frequency deviation extremum: (20) The time corresponding to the system's maximum frequency deviation: (twenty one) Example 4, refer to Figures 4-10 This invention provides a frequency response analysis method based on the inertia response of multiple types of resources, as one embodiment of the present invention. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0071] Based on the RTDS real-time simulation platform, a power system model including transmission and distribution networks was constructed, and its structural topology is as follows: Figure 4 As shown, the transmission network adopts the IEEE 3-machine 9-node standard example, where the three synchronous generators have capacities of 120MW, 150MW, and 120MW, respectively, and the load capacities at the three nodes are 100MW, 125MW, and 90MW, respectively. 225MW of new energy sources are integrated into the distribution network, and 225MW of loads are configured to simulate the low inertia characteristics caused by the high proportion of new energy integration in a new power system.

[0072] Virtual inertia control is applied to all distributed resources. The distribution network adopts a typical 30-node system, including 10 distributed photovoltaic nodes, 10 energy storage nodes, and 10 flexible load nodes. The total installed capacity of distributed photovoltaics is 75MW, the total capacity of energy storage devices is 75MW, and the total capacity of electric vehicle charging stations is 75MW. The system frequency response model parameters are identified as follows: Using the RTDS real-time simulation platform, power disturbances of 10MW, 20MW, and 30MW were applied to the constructed power grid system, and the frequency response curves of the system were obtained as follows: Figures 5-7 As shown.

[0073] Based on the system frequency response expression obtained by solving the frequency response model, the expression form of the curve to be fitted can be derived as follows: The frequency response characteristic curve data from the RTDS model is imported into Matlab, and the curve fitting toolbox is used to perform fitting, resulting in the following curve fitting results: Figures 8-10 As shown in Table 1.

[0074] Table 1 Fitting Results

[0075] Based on the system frequency response model parameter identification formula, analyze each item in the fitting results: Item: In the formula for identifying parameters of the corresponding frequency response model The term, whose value is related to the power disturbance applied to the system. The magnitude is directly proportional to the value of the term. After eliminating the influence of power disturbance, the mean value can be calculated to write the corresponding solution equation. Item: In the formula for identifying parameters of the corresponding frequency response model item; Item: In the formula for identifying parameters of the corresponding frequency response model item; Item: In the formula for identifying parameters of the corresponding frequency response model item, ; Item: In the formula for identifying parameters of the corresponding frequency response model Item, due to The phase of the equation is related to the initial value. Due to the time delay characteristics of the frequency response in the simulation, it is obtained after data processing. ; Therefore, based on the fitting results, the following system of equations can be written as follows: The above formula can only obtain four variables in the system frequency response model. However, as can be seen from the formula, there are six equivalent parameters in the system frequency response model that need to be identified. The solution equation obtained by applying a power disturbance is insufficient to identify all the equivalent parameters of the system frequency response model. In order to obtain the complete equivalent parameters of the system, the following additional conditions are given: 1) The droop control coefficient of the synchronous motor. ;2) Synchronous motor damping constant .

[0076] Therefore, based on the above additional conditions, the equivalent parameters of the system frequency response model are shown in Table 2.

[0077] Table 2 System Frequency Response Parameter Identification Values

[0078] Example 5 is an embodiment of the present invention. This embodiment provides a frequency response analysis system based on the inertia response of multiple types of resources, including: a virtual inertia control module, used to establish virtual inertia control strategies for photovoltaic systems, energy storage systems and electric vehicle charging stations respectively, and to obtain the inertia response coefficients of various types of resources; A frequency response model construction module is used to introduce a feedback adjustment link into a classic frequency response model. The feedback adjustment link sets a feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. The frequency deviation calculation module is used to establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and the power disturbance, and calculate the system frequency deviation under the power disturbance based on the relationship expression.

[0079] This embodiment also provides an electronic device applicable to a frequency response analysis method based on the inertia response of multiple types of resources, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the frequency response analysis method based on the inertia response of multiple types of resources as proposed in the above embodiment.

[0080] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a frequency response analysis method based on the inertia response of multiple types of resources as proposed in the above embodiment.

[0081] The storage medium proposed in this embodiment and the frequency response analysis method based on the inertia response of multiple types of resources proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0082] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A frequency response analysis method based on the inertia response of multiple resource types, characterized in that: include, Virtual inertia control strategies were established for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively, and the inertia response coefficients of various resources were obtained. A feedback adjustment loop is introduced into the classic frequency response model. The feedback adjustment loop sets the feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. Establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and power disturbance, and calculate the system frequency deviation under power disturbance based on the relationship expression.

2. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 1, characterized in that: The virtual inertia control strategies are established for photovoltaic systems, energy storage systems, and electric vehicle charging stations respectively, and the inertia response coefficients of various resources are obtained, including: For photovoltaic systems, a virtual inertia control strategy is established through the load reduction control of power electronic devices, and the inertia response coefficient of the photovoltaic system is obtained by utilizing the energy regulation characteristics of energy storage elements when the frequency changes. For energy storage systems, a virtual inertia control strategy is established based on a virtual synchronous generator model, and the inertia response coefficient of the energy storage system is obtained through the frequency dynamic response characteristics. For electric vehicle charging stations, a virtual inertia control strategy is established based on the power regulation capability of electric vehicle clusters, and the inertia response coefficient of the electric vehicle charging station is obtained through adjustable power calculation.

3. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 2, characterized in that: The introduction of a feedback adjustment mechanism into the classical frequency response model, wherein the feedback adjustment mechanism sets a feedback coefficient based on the inertia response coefficient, includes: The inertial response coefficients are converted into feedback coefficients using a parameter conversion method. The feedback coefficient is introduced into the feedback adjustment branch of the corresponding resource in the classical frequency response model; Set an inertial response dead zone so that the feedback adjustment circuit does not operate when the frequency fluctuation is lower than the preset dead zone threshold.

4. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 3, characterized in that: For photovoltaic systems, a virtual inertia control strategy is established through load shedding control of power electronic devices. The inertia response coefficient of the photovoltaic system is obtained by utilizing the energy regulation characteristics of energy storage elements during frequency changes. This includes: The photovoltaic operating voltage is controlled at a position higher than the optimal control voltage by load reduction control; The DC capacitor after the converter releases or stores energy through voltage fluctuations when the system frequency changes. Establish the correlation between the change in DC capacitor energy and the rate of frequency change, and determine the inertia response coefficient of the photovoltaic system based on the correlation.

5. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 4, characterized in that: The step of converting the inertial response coefficient into a feedback coefficient through parameter conversion includes: The photovoltaic feedback coefficient is obtained by multiplying the inertial response coefficient of the photovoltaic system by the preset conversion coefficient. The energy storage feedback coefficient is obtained by multiplying the inertial response coefficient of the energy storage system by the preset conversion coefficient. The electric vehicle feedback coefficient is obtained by multiplying the inertial response coefficient of the electric vehicle charging station by the preset conversion coefficient.

6. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 5, characterized in that: The process of solving the dynamic equation of the frequency response to obtain the relationship between the system frequency deviation and the power disturbance includes: The frequency response dynamic equation is transformed from the time domain to the frequency domain through mathematical transformation, resulting in a frequency domain expression for the frequency deviation. The power disturbance is expressed as a step function and substituted into the frequency domain expression; The frequency domain expression is converted into a time domain expression by inverse transformation, thus obtaining the mathematical relationship between frequency deviation and time.

7. The frequency response analysis method based on the inertia response of multiple resource types as described in claim 6, characterized in that: The calculation of the system's frequency deviation under power disturbance based on the relational expression includes: Differentiate the time-domain expression for the frequency deviation; The extreme values ​​of the frequency deviation and the corresponding time points are calculated using the derivative results. The frequency stability of the system is evaluated based on the extreme values ​​and time points.

8. A frequency response analysis system based on the inertia response of multiple resource types, employing the frequency response analysis method based on the inertia response of multiple resource types as described in any one of claims 1 to 7, characterized in that, include: The virtual inertia control module is used to establish virtual inertia control strategies for photovoltaic systems, energy storage systems, and electric vehicle charging stations, and to obtain the inertia response coefficients of various resources. A frequency response model construction module is used to introduce a feedback adjustment link into a classic frequency response model. The feedback adjustment link sets a feedback coefficient based on the inertia response coefficient to construct a frequency response model that includes multiple types of resources. The frequency deviation calculation module is used to establish the frequency response dynamic equation corresponding to the frequency response model, solve the frequency response dynamic equation to obtain the relationship expression between the system frequency deviation and the power disturbance, and calculate the system frequency deviation under the power disturbance based on the relationship expression.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the frequency response analysis method based on the inertia response of multiple types of resources as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the frequency response analysis method based on the inertia response of multiple types of resources as described in any one of claims 1 to 7.

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